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segmentation-3.0-b32-f16.mlmodelc/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7c76a1fc31b7a4d739f9112bf8ac55f4b5caad583d6ac9e47de680e775c9ade5
+size 243

segmentation-3.0-b32-f16.mlmodelc/coremldata.bin

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

segmentation-3.0-b32-f16.mlmodelc/model.mil

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+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]}}}})))] {
+            fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)];
+            string input_to_fp16_dtype_0 = const()[name = string("input_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1]> sincnet_wav_norm1d_weight_to_fp16 = const()[name = string("sincnet_wav_norm1d_weight_to_fp16"), val = tensor<fp16, [1]>([0x1.44p-7])];
+            tensor<fp16, [1]> sincnet_wav_norm1d_bias_to_fp16 = const()[name = string("sincnet_wav_norm1d_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.734p-5])];
+            fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [?, 1, 160000]> input_to_fp16 = cast(dtype = input_to_fp16_dtype_0, x = input)[name = string("cast_19")];
+            tensor<fp16, [?, 1, 160000]> waveform_cast_fp16 = instance_norm(beta = sincnet_wav_norm1d_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_wav_norm1d_weight_to_fp16, x = input_to_fp16)[name = string("waveform_cast_fp16")];
+            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<fp16, [80, 1, 251]> filters_to_fp16 = const()[name = string("filters_to_fp16"), val = tensor<fp16, [80, 1, 251]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [?, 80, 15975]> outputs_cast_fp16 = 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_to_fp16, x = waveform_cast_fp16)[name = string("outputs_cast_fp16")];
+            tensor<fp16, [?, 80, 15975]> input_1_cast_fp16 = abs(x = outputs_cast_fp16)[name = string("input_1_cast_fp16")];
+            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<fp16, [?, 80, 5325]> input_3_cast_fp16 = 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_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<fp16, [80]> sincnet_norm1d_0_weight_to_fp16 = const()[name = string("sincnet_norm1d_0_weight_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40320)))];
+            tensor<fp16, [80]> sincnet_norm1d_0_bias_to_fp16 = const()[name = string("sincnet_norm1d_0_bias_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40576)))];
+            tensor<fp16, [?, 80, 5325]> input_5_cast_fp16 = instance_norm(beta = sincnet_norm1d_0_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<fp16, [?, 80, 5325]> input_7_cast_fp16 = leaky_relu(alpha = var_9, 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("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<fp16, [60, 80, 5]> sincnet_conv1d_1_weight_to_fp16 = const()[name = string("sincnet_conv1d_1_weight_to_fp16"), val = tensor<fp16, [60, 80, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40832)))];
+            tensor<fp16, [60]> sincnet_conv1d_1_bias_to_fp16 = const()[name = string("sincnet_conv1d_1_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88896)))];
+            tensor<fp16, [?, 60, 5321]> input_9_cast_fp16 = conv(bias = sincnet_conv1d_1_bias_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 = sincnet_conv1d_1_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")];
+            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<fp16, [?, 60, 1773]> input_11_cast_fp16 = 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_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [60]> sincnet_norm1d_1_weight_to_fp16 = const()[name = string("sincnet_norm1d_1_weight_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89088)))];
+            tensor<fp16, [60]> sincnet_norm1d_1_bias_to_fp16 = const()[name = string("sincnet_norm1d_1_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89280)))];
+            tensor<fp16, [?, 60, 1773]> input_13_cast_fp16 = instance_norm(beta = sincnet_norm1d_1_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_1_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            tensor<fp16, [?, 60, 1773]> input_15_cast_fp16 = leaky_relu(alpha = var_9, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
+            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<fp16, [60, 60, 5]> sincnet_conv1d_2_weight_to_fp16 = const()[name = string("sincnet_conv1d_2_weight_to_fp16"), val = tensor<fp16, [60, 60, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89472)))];
+            tensor<fp16, [60]> sincnet_conv1d_2_bias_to_fp16 = const()[name = string("sincnet_conv1d_2_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568)))];
+            tensor<fp16, [?, 60, 1769]> input_17_cast_fp16 = conv(bias = sincnet_conv1d_2_bias_to_fp16, 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_to_fp16, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            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<fp16, [?, 60, 589]> input_19_cast_fp16 = 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_cast_fp16)[name = string("input_19_cast_fp16")];
+            tensor<fp16, [60]> sincnet_norm1d_2_weight_to_fp16 = const()[name = string("sincnet_norm1d_2_weight_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125760)))];
+            tensor<fp16, [60]> sincnet_norm1d_2_bias_to_fp16 = const()[name = string("sincnet_norm1d_2_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125952)))];
+            tensor<fp16, [?, 60, 589]> input_21_cast_fp16 = instance_norm(beta = sincnet_norm1d_2_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_2_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
+            tensor<fp16, [?, 60, 589]> x_cast_fp16 = leaky_relu(alpha = var_9, x = input_21_cast_fp16)[name = string("x_cast_fp16")];
+            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<fp16, [?, 589, 60]> input_23_cast_fp16 = transpose(perm = var_163, x = x_cast_fp16)[name = string("transpose_6")];
+            tensor<int32, [3]> var_207_shape_cast_fp16 = shape(x = input_23_cast_fp16)[name = string("op_207_shape_cast_fp16")];
+            int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)];
+            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)];
+            string var_207_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_207_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")];
+            uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(0)];
+            tensor<int16, [3]> var_207_shape_cast_fp16_to_int16 = cast(dtype = var_207_shape_cast_fp16_to_int16_dtype_0, x = var_207_shape_cast_fp16)[name = string("cast_18")];
+            int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_207_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")];
+            string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")];
+            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)];
+            int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_17")];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0_cast_uint16_to_int32, var_172))[name = string("concat_0")];
+            fp16 hx_1_value_0_to_fp16 = const()[name = string("hx_1_value_0_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [8, ?, 128]> hx_1_cast_fp16 = fill(shape = concat_0, value = hx_1_value_0_to_fp16)[name = string("hx_1_cast_fp16")];
+            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<fp16, [2, ?, 128]> split_0_cast_fp16_0, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_1, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_2, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1_cast_fp16)[name = string("split_0_cast_fp16")];
+            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<fp16, [2, ?, 128]> split_1_cast_fp16_0, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_1, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_2, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1_cast_fp16)[name = string("split_1_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_10_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_10_cast_fp16_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_cast_fp16_0)[name = string("split_10_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_10_cast_fp16 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_cast_fp16_0, split_10_cast_fp16_1))[name = string("concat_10_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_11_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_11_cast_fp16_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_cast_fp16_0)[name = string("split_11_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_11_cast_fp16 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_cast_fp16_0, split_11_cast_fp16_1))[name = string("concat_11_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 60]> concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor<fp16, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126144)))];
+            tensor<fp16, [512, 128]> concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187648)))];
+            tensor<fp16, [512]> add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318784)))];
+            tensor<fp16, [512, 60]> concat_8_to_fp16 = const()[name = string("concat_8_to_fp16"), val = tensor<fp16, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319872)))];
+            tensor<fp16, [512, 128]> concat_9_to_fp16 = const()[name = string("concat_9_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381376)))];
+            tensor<fp16, [512]> add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512512)))];
+            tensor<fp16, [589, ?, 60]> input_23_batch_first_transpose_cast_fp16 = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23_cast_fp16)[name = string("transpose_5")];
+            tensor<fp16, [589, ?, 256]> input_25_lstm_layer_0_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_0_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_0_cast_fp16_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, 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_cast_fp16, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7_to_fp16, weight_hh_back = concat_9_to_fp16, weight_ih = concat_6_to_fp16, weight_ih_back = concat_8_to_fp16, x = input_23_batch_first_transpose_cast_fp16)[name = string("input_25_lstm_layer_0_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_20_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_20_cast_fp16_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_cast_fp16_1)[name = string("split_20_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_20_cast_fp16 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_cast_fp16_0, split_20_cast_fp16_1))[name = string("concat_20_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_21_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_21_cast_fp16_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_cast_fp16_1)[name = string("split_21_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_21_cast_fp16 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_cast_fp16_0, split_21_cast_fp16_1))[name = string("concat_21_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 256]> concat_16_to_fp16 = const()[name = string("concat_16_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513600)))];
+            tensor<fp16, [512, 128]> concat_17_to_fp16 = const()[name = string("concat_17_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775808)))];
+            tensor<fp16, [512]> add_2_to_fp16 = const()[name = string("add_2_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906944)))];
+            tensor<fp16, [512, 256]> concat_18_to_fp16 = const()[name = string("concat_18_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908032)))];
+            tensor<fp16, [512, 128]> concat_19_to_fp16 = const()[name = string("concat_19_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1170240)))];
+            tensor<fp16, [512]> add_3_to_fp16 = const()[name = string("add_3_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1301376)))];
+            tensor<fp16, [589, ?, 256]> input_25_lstm_layer_1_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_1_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_1_cast_fp16_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, 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_cast_fp16, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17_to_fp16, weight_hh_back = concat_19_to_fp16, weight_ih = concat_16_to_fp16, weight_ih_back = concat_18_to_fp16, x = input_25_lstm_layer_0_cast_fp16_0)[name = string("input_25_lstm_layer_1_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_30_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_30_cast_fp16_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_cast_fp16_2)[name = string("split_30_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_30_cast_fp16 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_cast_fp16_0, split_30_cast_fp16_1))[name = string("concat_30_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_31_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_31_cast_fp16_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_cast_fp16_2)[name = string("split_31_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_31_cast_fp16 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_cast_fp16_0, split_31_cast_fp16_1))[name = string("concat_31_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 256]> concat_26_to_fp16 = const()[name = string("concat_26_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302464)))];
+            tensor<fp16, [512, 128]> concat_27_to_fp16 = const()[name = string("concat_27_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1564672)))];
+            tensor<fp16, [512]> add_4_to_fp16 = const()[name = string("add_4_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1695808)))];
+            tensor<fp16, [512, 256]> concat_28_to_fp16 = const()[name = string("concat_28_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1696896)))];
+            tensor<fp16, [512, 128]> concat_29_to_fp16 = const()[name = string("concat_29_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1959104)))];
+            tensor<fp16, [512]> add_5_to_fp16 = const()[name = string("add_5_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2090240)))];
+            tensor<fp16, [589, ?, 256]> input_25_lstm_layer_2_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_2_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_2_cast_fp16_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, 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_cast_fp16, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27_to_fp16, weight_hh_back = concat_29_to_fp16, weight_ih = concat_26_to_fp16, weight_ih_back = concat_28_to_fp16, x = input_25_lstm_layer_1_cast_fp16_0)[name = string("input_25_lstm_layer_2_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_40_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_40_cast_fp16_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_cast_fp16_3)[name = string("split_40_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_40_cast_fp16 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_cast_fp16_0, split_40_cast_fp16_1))[name = string("concat_40_cast_fp16")];
+            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<fp16, [?, 256]> input_25_batch_first_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40_cast_fp16)[name = string("input_25_batch_first_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_41_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_41_cast_fp16_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_cast_fp16_3)[name = string("split_41_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_41_cast_fp16 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_cast_fp16_0, split_41_cast_fp16_1))[name = string("concat_41_cast_fp16")];
+            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<fp16, [?, 256]> input_25_batch_first_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41_cast_fp16)[name = string("input_25_batch_first_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 256]> concat_36_to_fp16 = const()[name = string("concat_36_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2091328)))];
+            tensor<fp16, [512, 128]> concat_37_to_fp16 = const()[name = string("concat_37_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2353536)))];
+            tensor<fp16, [512]> add_6_to_fp16 = const()[name = string("add_6_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2484672)))];
+            tensor<fp16, [512, 256]> concat_38_to_fp16 = const()[name = string("concat_38_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2485760)))];
+            tensor<fp16, [512, 128]> concat_39_to_fp16 = const()[name = string("concat_39_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2747968)))];
+            tensor<fp16, [512]> add_7_to_fp16 = const()[name = string("add_7_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2879104)))];
+            tensor<fp16, [589, ?, 256]> input_25_batch_first_cast_fp16_0, tensor<fp16, [?, 256]> input_25_batch_first_cast_fp16_1, tensor<fp16, [?, 256]> input_25_batch_first_cast_fp16_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, 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_cast_fp16, initial_h = input_25_batch_first_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37_to_fp16, weight_hh_back = concat_39_to_fp16, weight_ih = concat_36_to_fp16, weight_ih_back = concat_38_to_fp16, x = input_25_lstm_layer_2_cast_fp16_0)[name = string("input_25_batch_first_cast_fp16")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [128, 256]> linear_0_weight_to_fp16 = const()[name = string("linear_0_weight_to_fp16"), val = tensor<fp16, [128, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2880192)))];
+            tensor<fp16, [128]> linear_0_bias_to_fp16 = const()[name = string("linear_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2945792)))];
+            tensor<fp16, [?, 589, 256]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = input_25_batch_first_cast_fp16_0)[name = string("transpose_4")];
+            tensor<fp16, [?, 589, 128]> linear_0_cast_fp16 = linear(bias = linear_0_bias_to_fp16, weight = linear_0_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")];
+            fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp16, [?, 589, 128]> input_29_cast_fp16 = leaky_relu(alpha = var_220, x = linear_0_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [128, 128]> linear_1_weight_to_fp16 = const()[name = string("linear_1_weight_to_fp16"), val = tensor<fp16, [128, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2946112)))];
+            tensor<fp16, [128]> linear_1_bias_to_fp16 = const()[name = string("linear_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2978944)))];
+            tensor<fp16, [?, 589, 128]> linear_1_cast_fp16 = linear(bias = linear_1_bias_to_fp16, weight = linear_1_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")];
+            fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp16, [?, 589, 128]> input_33_cast_fp16 = leaky_relu(alpha = var_225, x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [7, 128]> classifier_weight_to_fp16 = const()[name = string("classifier_weight_to_fp16"), val = tensor<fp16, [7, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2979264)))];
+            tensor<fp16, [7]> classifier_bias_to_fp16 = const()[name = string("classifier_bias_to_fp16"), val = tensor<fp16, [7]>([-0x1.01p+0, 0x1.67cp-2, 0x1.3d8p-1, 0x1.c8cp-2, -0x1.444p-2, -0x1.59p-1, -0x1.8fcp-2])];
+            tensor<fp16, [?, 589, 7]> linear_2_cast_fp16 = linear(bias = classifier_bias_to_fp16, weight = classifier_weight_to_fp16, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")];
+            int32 var_231 = const()[name = string("op_231"), val = int32(-1)];
+            tensor<fp16, [?, 589, 7]> var_232_softmax_cast_fp16 = softmax(axis = var_231, x = linear_2_cast_fp16)[name = string("op_232_softmax_cast_fp16")];
+            fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [?, 589, 7]> var_232_cast_fp16 = log(epsilon = var_232_epsilon_0, x = var_232_softmax_cast_fp16)[name = string("op_232_cast_fp16")];
+            string var_232_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_232_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [?, 589, 7]> output = cast(dtype = var_232_cast_fp16_to_fp32_dtype_0, x = var_232_cast_fp16)[name = string("cast_16")];
+        } -> (output);
+}

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segmentation-3.0-b32-w8a16.mlmodelc/coremldata.bin

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segmentation-3.0-b32-w8a16.mlmodelc/model.mil

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+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_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [60, 80, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor<fp32, [60, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25216))))[name = string("sincnet_conv1d_1_weight_quantized")];
+            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(25536)))];
+            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(25856)))];
+            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(26176)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [60, 60, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26496))), scale = tensor<fp32, [60, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44608))))[name = string("sincnet_conv1d_2_weight_quantized")];
+            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(44928)))];
+            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(45248)))];
+            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(45568)))];
+            tensor<fp32, [128, 256]> linear_0_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46144))), scale = tensor<fp32, [128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78976))))[name = string("linear_0_weight_quantized")];
+            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(79552)))];
+            tensor<fp32, [128, 128]> linear_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80128))), scale = tensor<fp32, [128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96576))))[name = string("linear_1_weight_quantized")];
+            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(97152)))];
+            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_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [80, 1, 251]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100800))), scale = tensor<fp32, [80, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120960))))[name = string("filters_quantized")];
+            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_quantized, 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_quantized, 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_quantized, 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_2")];
+            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_0 = const()[name = string("gather_0_axis_0"), val = int32(0)];
+            int32 gather_0 = gather(axis = gather_0_axis_0, 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(121344)))];
+            tensor<fp32, [512]> add_1 = const()[name = string("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123456)))];
+            tensor<fp32, [512, 60]> concat_6_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156352))))[name = string("concat_6_quantized")];
+            tensor<fp32, [512, 128]> concat_7_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158464))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224064))))[name = string("concat_7_quantized")];
+            tensor<fp32, [512, 60]> concat_8_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256960))))[name = string("concat_8_quantized")];
+            tensor<fp32, [512, 128]> concat_9_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259072))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324672))))[name = string("concat_9_quantized")];
+            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_1")];
+            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_quantized, weight_hh_back = concat_9_quantized, weight_ih = concat_6_quantized, weight_ih_back = concat_8_quantized, 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(326784)))];
+            tensor<fp32, [512]> add_3 = const()[name = string("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328896)))];
+            tensor<fp32, [512, 256]> concat_16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331008))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462144))))[name = string("concat_16_quantized")];
+            tensor<fp32, [512, 128]> concat_17_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464256))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529856))))[name = string("concat_17_quantized")];
+            tensor<fp32, [512, 256]> concat_18_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531968))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(663104))))[name = string("concat_18_quantized")];
+            tensor<fp32, [512, 128]> concat_19_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(665216))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(730816))))[name = string("concat_19_quantized")];
+            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_quantized, weight_hh_back = concat_19_quantized, weight_ih = concat_16_quantized, weight_ih_back = concat_18_quantized, 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(732928)))];
+            tensor<fp32, [512]> add_5 = const()[name = string("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(735040)))];
+            tensor<fp32, [512, 256]> concat_26_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(737152))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(868288))))[name = string("concat_26_quantized")];
+            tensor<fp32, [512, 128]> concat_27_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(870400))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[name = string("concat_27_quantized")];
+            tensor<fp32, [512, 256]> concat_28_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(938112))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069248))))[name = string("concat_28_quantized")];
+            tensor<fp32, [512, 128]> concat_29_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1071360))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136960))))[name = string("concat_29_quantized")];
+            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_quantized, weight_hh_back = concat_29_quantized, weight_ih = concat_26_quantized, weight_ih_back = concat_28_quantized, 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(1139072)))];
+            tensor<fp32, [512]> add_7 = const()[name = string("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1141184)))];
+            tensor<fp32, [512, 256]> concat_36_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1143296))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274432))))[name = string("concat_36_quantized")];
+            tensor<fp32, [512, 128]> concat_37_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1276544))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1342144))))[name = string("concat_37_quantized")];
+            tensor<fp32, [512, 256]> concat_38_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1344256))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475392))))[name = string("concat_38_quantized")];
+            tensor<fp32, [512, 128]> concat_39_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1477504))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1543104))))[name = string("concat_39_quantized")];
+            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_quantized, weight_hh_back = concat_39_quantized, weight_ih = concat_36_quantized, weight_ih_back = concat_38_quantized, 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_0")];
+            tensor<fp32, [?, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, 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_quantized, 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);
+}

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segmentation-3.0-b56-w8a16.mlmodelc/analytics/coremldata.bin

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segmentation-3.0-b56-w8a16.mlmodelc/coremldata.bin

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segmentation-3.0-b56-w8a16.mlmodelc/model.mil

@@ -0,0 +1,135 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios16>(tensor<fp32, [56, 1, 160000]> input) {
+            tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = tensor<string, []>("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
+            tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = tensor<string, []>("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
+            tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = tensor<string, []>("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = tensor<string, []>("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
+            tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = tensor<string, []>("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
+            tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("sincnet_conv1d_1_weight_quantized"), quantized_data = tensor<int8, [60, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152))), scale = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25344))), zero_point = tensor<int8, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25216)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = tensor<string, []>("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25664)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = tensor<string, []>("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25984)))];
+            tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = tensor<string, []>("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26304)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("sincnet_conv1d_2_weight_quantized"), quantized_data = tensor<int8, [60, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26624))), scale = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44864))), zero_point = tensor<int8, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44736)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = tensor<string, []>("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45184)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = tensor<string, []>("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45504)))];
+            tensor<fp32, [128]> linear_0_bias = const()[name = tensor<string, []>("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45824)))];
+            tensor<fp32, [128, 256]> linear_0_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("linear_0_weight_quantized"), quantized_data = tensor<int8, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46400))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79424))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79232)))];
+            tensor<fp32, [128]> linear_1_bias = const()[name = tensor<string, []>("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80000)))];
+            tensor<fp32, [128, 128]> linear_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("linear_1_weight_quantized"), quantized_data = tensor<int8, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80576))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97024))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79232)))];
+            tensor<fp32, [7]> classifier_bias = const()[name = tensor<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_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("classifier_weight_quantized"), quantized_data = tensor<int8, [7, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97600))), scale = tensor<fp32, [7]>([0x1.a91b44p-7, 0x1.26991cp-7, 0x1.8bd9b2p-7, 0x1.0eb8bp-7, 0x1.4a7844p-7, 0x1.3ccf28p-6, 0x1.5ebed6p-7]), zero_point = tensor<int8, [7]>([0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<fp32, [56, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor<string, []>("waveform")];
+            tensor<fp32, [80, 1, 251]> filters_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("filters_quantized"), quantized_data = tensor<int8, [80, 1, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98560))), scale = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118912))), zero_point = tensor<int8, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118720)))];
+            tensor<string, []> outputs_pad_type_0 = const()[name = tensor<string, []>("outputs_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> outputs_strides_0 = const()[name = tensor<string, []>("outputs_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [2]> outputs_pad_0 = const()[name = tensor<string, []>("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> outputs_dilations_0 = const()[name = tensor<string, []>("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> outputs_groups_0 = const()[name = tensor<string, []>("outputs_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [56, 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_quantized, x = waveform)[name = tensor<string, []>("outputs")];
+            tensor<fp32, [56, 80, 15975]> input_1 = abs(x = outputs)[name = tensor<string, []>("input_1")];
+            tensor<int32, [1]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_3_ceil_mode_0 = const()[name = tensor<string, []>("input_3_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_3")];
+            tensor<fp32, [56, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor<string, []>("input_5")];
+            tensor<fp32, [56, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor<string, []>("input_7")];
+            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [56, 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_quantized, x = input_7)[name = tensor<string, []>("input_9")];
+            tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_136 = const()[name = tensor<string, []>("op_136"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_11_ceil_mode_0 = const()[name = tensor<string, []>("input_11_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_11")];
+            tensor<fp32, [56, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor<string, []>("input_13")];
+            tensor<fp32, [56, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor<string, []>("input_15")];
+            tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [56, 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_quantized, x = input_15)[name = tensor<string, []>("input_17")];
+            tensor<int32, [1]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_19_ceil_mode_0 = const()[name = tensor<string, []>("input_19_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_19")];
+            tensor<fp32, [56, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor<string, []>("input_21")];
+            tensor<fp32, [56, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = tensor<string, []>("x")];
+            tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
+            tensor<fp32, [512]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(119296)))];
+            tensor<fp32, [512]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121408)))];
+            tensor<fp32, [512, 60]> concat_4_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_4_quantized"), quantized_data = tensor<int8, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123520))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154880))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_5_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_5_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156992))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222592))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 60]> concat_6_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_6_quantized"), quantized_data = tensor<int8, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224704))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255488))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_7_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_7_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257600))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323200))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [56, 256]> input_25_lstm_layer_0_lstm_h0_reshaped_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped_quantized"), quantized_data = tensor<int8, [56, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325312))), scale = tensor<fp32, [56]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(339840))), zero_point = tensor<int8, [56]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(339712)))];
+            tensor<string, []> input_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 60]> transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor<string, []>("transpose_1")];
+            tensor<fp32, [589, 56, 256]> input_25_lstm_layer_0_0, tensor<fp32, [56, 256]> input_25_lstm_layer_0_1, tensor<fp32, [56, 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_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5_quantized, weight_hh_back = concat_7_quantized, weight_ih = concat_4_quantized, weight_ih_back = concat_6_quantized, x = transpose_4)[name = tensor<string, []>("input_25_lstm_layer_0")];
+            tensor<fp32, [512]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(340160)))];
+            tensor<fp32, [512]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(342272)))];
+            tensor<fp32, [512, 256]> concat_14_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_14_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344384))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(475520))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_15_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_15_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477632))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(543232))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 256]> concat_16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_16_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545344))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(676480))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_17_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_17_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678592))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(744192))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<string, []> input_25_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 256]> input_25_lstm_layer_1_0, tensor<fp32, [56, 256]> input_25_lstm_layer_1_1, tensor<fp32, [56, 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_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15_quantized, weight_hh_back = concat_17_quantized, weight_ih = concat_14_quantized, weight_ih_back = concat_16_quantized, x = input_25_lstm_layer_0_0)[name = tensor<string, []>("input_25_lstm_layer_1")];
+            tensor<fp32, [512]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(746304)))];
+            tensor<fp32, [512]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748416)))];
+            tensor<fp32, [512, 256]> concat_24_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_24_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750528))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(881664))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_25_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_25_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(883776))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(949376))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 256]> concat_26_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_26_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(951488))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1082624))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_27_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_27_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1084736))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1150336))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<string, []> input_25_lstm_layer_2_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_2_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 256]> input_25_lstm_layer_2_0, tensor<fp32, [56, 256]> input_25_lstm_layer_2_1, tensor<fp32, [56, 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_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25_quantized, weight_hh_back = concat_27_quantized, weight_ih = concat_24_quantized, weight_ih_back = concat_26_quantized, x = input_25_lstm_layer_1_0)[name = tensor<string, []>("input_25_lstm_layer_2")];
+            tensor<fp32, [512]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152448)))];
+            tensor<fp32, [512]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1154560)))];
+            tensor<fp32, [512, 256]> concat_34_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_34_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1156672))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1287808))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_35_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_35_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1289920))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1355520))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 256]> concat_36_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_36_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357632))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1488768))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_37_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_37_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1490880))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1556480))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<string, []> input_25_batch_first_direction_0 = const()[name = tensor<string, []>("input_25_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_25_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_batch_first_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 256]> input_25_batch_first_0, tensor<fp32, [56, 256]> input_25_batch_first_1, tensor<fp32, [56, 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_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35_quantized, weight_hh_back = concat_37_quantized, weight_ih = concat_34_quantized, weight_ih_back = concat_36_quantized, x = input_25_lstm_layer_2_0)[name = tensor<string, []>("input_25_batch_first")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp32, [56, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor<string, []>("transpose_0")];
+            tensor<fp32, [56, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, x = input_25)[name = tensor<string, []>("linear_0")];
+            tensor<fp32, []> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [56, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor<string, []>("input_29")];
+            tensor<fp32, [56, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight_quantized, x = input_29)[name = tensor<string, []>("linear_1")];
+            tensor<fp32, []> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [56, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor<string, []>("input_33")];
+            tensor<fp32, [56, 589, 7]> input_1_1 = linear(bias = classifier_bias, weight = classifier_weight_quantized, x = input_33)[name = tensor<string, []>("linear_2")];
+            tensor<int32, []> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, []>(-1)];
+            tensor<fp32, [56, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor<string, []>("op_232_softmax")];
+            tensor<fp32, []> var_232_epsilon_0 = const()[name = tensor<string, []>("op_232_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [56, 589, 7]> output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor<string, []>("op_232")];
+        } -> (output);
+}

segmentation-3.0-b56-w8a16.mlmodelc/weights/weight.bin

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+size 1558592

segmentation-3.0-b56.mlmodelc/analytics/coremldata.bin

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+size 243

segmentation-3.0-b56.mlmodelc/coremldata.bin

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segmentation-3.0-b56.mlmodelc/model.mil

@@ -0,0 +1,135 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios16>(tensor<fp32, [56, 1, 160000]> input) {
+            tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = tensor<string, []>("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
+            tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = tensor<string, []>("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
+            tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = tensor<string, []>("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = tensor<string, []>("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
+            tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = tensor<string, []>("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
+            tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight = const()[name = tensor<string, []>("sincnet_conv1d_1_weight"), val = tensor<fp32, [60, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = tensor<string, []>("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97216)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = tensor<string, []>("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97536)))];
+            tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = tensor<string, []>("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97856)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight = const()[name = tensor<string, []>("sincnet_conv1d_2_weight"), val = tensor<fp32, [60, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98176)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = tensor<string, []>("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170240)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = tensor<string, []>("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170560)))];
+            tensor<fp32, [128]> linear_0_bias = const()[name = tensor<string, []>("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170880)))];
+            tensor<fp32, [128, 256]> linear_0_weight = const()[name = tensor<string, []>("linear_0_weight"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171456)))];
+            tensor<fp32, [128]> linear_1_bias = const()[name = tensor<string, []>("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302592)))];
+            tensor<fp32, [128, 128]> linear_1_weight = const()[name = tensor<string, []>("linear_1_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303168)))];
+            tensor<fp32, [7]> classifier_bias = const()[name = tensor<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 = tensor<string, []>("classifier_weight"), val = tensor<fp32, [7, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368768)))];
+            tensor<fp32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<fp32, [56, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor<string, []>("waveform")];
+            tensor<fp32, [80, 1, 251]> filters = const()[name = tensor<string, []>("filters"), val = tensor<fp32, [80, 1, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372416)))];
+            tensor<string, []> outputs_pad_type_0 = const()[name = tensor<string, []>("outputs_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> outputs_strides_0 = const()[name = tensor<string, []>("outputs_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [2]> outputs_pad_0 = const()[name = tensor<string, []>("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> outputs_dilations_0 = const()[name = tensor<string, []>("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> outputs_groups_0 = const()[name = tensor<string, []>("outputs_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [56, 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 = tensor<string, []>("outputs")];
+            tensor<fp32, [56, 80, 15975]> input_1 = abs(x = outputs)[name = tensor<string, []>("input_1")];
+            tensor<int32, [1]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_3_ceil_mode_0 = const()[name = tensor<string, []>("input_3_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_3")];
+            tensor<fp32, [56, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor<string, []>("input_5")];
+            tensor<fp32, [56, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor<string, []>("input_7")];
+            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_9")];
+            tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_136 = const()[name = tensor<string, []>("op_136"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_11_ceil_mode_0 = const()[name = tensor<string, []>("input_11_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_11")];
+            tensor<fp32, [56, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor<string, []>("input_13")];
+            tensor<fp32, [56, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor<string, []>("input_15")];
+            tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_17")];
+            tensor<int32, [1]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_19_ceil_mode_0 = const()[name = tensor<string, []>("input_19_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [56, 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 = tensor<string, []>("input_19")];
+            tensor<fp32, [56, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor<string, []>("input_21")];
+            tensor<fp32, [56, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = tensor<string, []>("x")];
+            tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
+            tensor<fp32, [512]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452800)))];
+            tensor<fp32, [512]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454912)))];
+            tensor<fp32, [512, 60]> concat_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457024)))];
+            tensor<fp32, [512, 128]> concat_5 = const()[name = tensor<string, []>("concat_5"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579968)))];
+            tensor<fp32, [512, 60]> concat_6 = const()[name = tensor<string, []>("concat_6"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842176)))];
+            tensor<fp32, [512, 128]> concat_7 = const()[name = tensor<string, []>("concat_7"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(965120)))];
+            tensor<fp32, [56, 256]> input_25_lstm_layer_0_lstm_h0_reshaped = const()[name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped"), val = tensor<fp32, [56, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227328)))];
+            tensor<string, []> input_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 60]> transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor<string, []>("transpose_6")];
+            tensor<fp32, [589, 56, 256]> input_25_lstm_layer_0_0, tensor<fp32, [56, 256]> input_25_lstm_layer_0_1, tensor<fp32, [56, 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_h0_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_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = transpose_4)[name = tensor<string, []>("input_25_lstm_layer_0")];
+            tensor<fp32, [512]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1284736)))];
+            tensor<fp32, [512]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1286848)))];
+            tensor<fp32, [512, 256]> concat_14 = const()[name = tensor<string, []>("concat_14"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1288960)))];
+            tensor<fp32, [512, 128]> concat_15 = const()[name = tensor<string, []>("concat_15"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1813312)))];
+            tensor<fp32, [512, 256]> concat_16 = const()[name = tensor<string, []>("concat_16"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2075520)))];
+            tensor<fp32, [512, 128]> concat_17 = const()[name = tensor<string, []>("concat_17"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2599872)))];
+            tensor<string, []> input_25_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 256]> input_25_lstm_layer_1_0, tensor<fp32, [56, 256]> input_25_lstm_layer_1_1, tensor<fp32, [56, 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_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_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_15, weight_hh_back = concat_17, weight_ih = concat_14, weight_ih_back = concat_16, x = input_25_lstm_layer_0_0)[name = tensor<string, []>("input_25_lstm_layer_1")];
+            tensor<fp32, [512]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2862080)))];
+            tensor<fp32, [512]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2864192)))];
+            tensor<fp32, [512, 256]> concat_24 = const()[name = tensor<string, []>("concat_24"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2866304)))];
+            tensor<fp32, [512, 128]> concat_25 = const()[name = tensor<string, []>("concat_25"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3390656)))];
+            tensor<fp32, [512, 256]> concat_26 = const()[name = tensor<string, []>("concat_26"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3652864)))];
+            tensor<fp32, [512, 128]> concat_27 = const()[name = tensor<string, []>("concat_27"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4177216)))];
+            tensor<string, []> input_25_lstm_layer_2_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_2_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 256]> input_25_lstm_layer_2_0, tensor<fp32, [56, 256]> input_25_lstm_layer_2_1, tensor<fp32, [56, 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_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_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_25, weight_hh_back = concat_27, weight_ih = concat_24, weight_ih_back = concat_26, x = input_25_lstm_layer_1_0)[name = tensor<string, []>("input_25_lstm_layer_2")];
+            tensor<fp32, [512]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4439424)))];
+            tensor<fp32, [512]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4441536)))];
+            tensor<fp32, [512, 256]> concat_34 = const()[name = tensor<string, []>("concat_34"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4443648)))];
+            tensor<fp32, [512, 128]> concat_35 = const()[name = tensor<string, []>("concat_35"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4968000)))];
+            tensor<fp32, [512, 256]> concat_36 = const()[name = tensor<string, []>("concat_36"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5230208)))];
+            tensor<fp32, [512, 128]> concat_37 = const()[name = tensor<string, []>("concat_37"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5754560)))];
+            tensor<string, []> input_25_batch_first_direction_0 = const()[name = tensor<string, []>("input_25_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_25_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_batch_first_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 56, 256]> input_25_batch_first_0, tensor<fp32, [56, 256]> input_25_batch_first_1, tensor<fp32, [56, 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_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35, weight_hh_back = concat_37, weight_ih = concat_34, weight_ih_back = concat_36, x = input_25_lstm_layer_2_0)[name = tensor<string, []>("input_25_batch_first")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp32, [56, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor<string, []>("transpose_5")];
+            tensor<fp32, [56, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = tensor<string, []>("linear_0")];
+            tensor<fp32, []> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [56, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor<string, []>("input_29")];
+            tensor<fp32, [56, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = tensor<string, []>("linear_1")];
+            tensor<fp32, []> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [56, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor<string, []>("input_33")];
+            tensor<fp32, [56, 589, 7]> input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = tensor<string, []>("linear_2")];
+            tensor<int32, []> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, []>(-1)];
+            tensor<fp32, [56, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor<string, []>("op_232_softmax")];
+            tensor<fp32, []> var_232_epsilon_0 = const()[name = tensor<string, []>("op_232_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [56, 589, 7]> output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor<string, []>("op_232")];
+        } -> (output);
+}

segmentation-3.0-b56.mlmodelc/weights/weight.bin

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segmentation-3.0-b64-w8a16.mlmodelc/analytics/coremldata.bin

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segmentation-3.0-b64-w8a16.mlmodelc/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
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segmentation-3.0-b64-w8a16.mlmodelc/model.mil

@@ -0,0 +1,135 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios16>(tensor<fp32, [64, 1, 160000]> input) {
+            tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = tensor<string, []>("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
+            tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = tensor<string, []>("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
+            tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = tensor<string, []>("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = tensor<string, []>("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
+            tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = tensor<string, []>("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
+            tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("sincnet_conv1d_1_weight_quantized"), quantized_data = tensor<int8, [60, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152))), scale = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25344))), zero_point = tensor<int8, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25216)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = tensor<string, []>("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25664)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = tensor<string, []>("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(25984)))];
+            tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = tensor<string, []>("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26304)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("sincnet_conv1d_2_weight_quantized"), quantized_data = tensor<int8, [60, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26624))), scale = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44864))), zero_point = tensor<int8, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(44736)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = tensor<string, []>("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45184)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = tensor<string, []>("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45504)))];
+            tensor<fp32, [128]> linear_0_bias = const()[name = tensor<string, []>("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(45824)))];
+            tensor<fp32, [128, 256]> linear_0_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("linear_0_weight_quantized"), quantized_data = tensor<int8, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(46400))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79424))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79232)))];
+            tensor<fp32, [128]> linear_1_bias = const()[name = tensor<string, []>("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80000)))];
+            tensor<fp32, [128, 128]> linear_1_weight_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("linear_1_weight_quantized"), quantized_data = tensor<int8, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(80576))), scale = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97024))), zero_point = tensor<int8, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79232)))];
+            tensor<fp32, [7]> classifier_bias = const()[name = tensor<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_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("classifier_weight_quantized"), quantized_data = tensor<int8, [7, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97600))), scale = tensor<fp32, [7]>([0x1.a91b44p-7, 0x1.26991cp-7, 0x1.8bd9b2p-7, 0x1.0eb8bp-7, 0x1.4a7844p-7, 0x1.3ccf28p-6, 0x1.5ebed6p-7]), zero_point = tensor<int8, [7]>([0, 0, 0, 0, 0, 0, 0])];
+            tensor<fp32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<fp32, [64, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor<string, []>("waveform")];
+            tensor<fp32, [80, 1, 251]> filters_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("filters_quantized"), quantized_data = tensor<int8, [80, 1, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98560))), scale = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118912))), zero_point = tensor<int8, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(118720)))];
+            tensor<string, []> outputs_pad_type_0 = const()[name = tensor<string, []>("outputs_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> outputs_strides_0 = const()[name = tensor<string, []>("outputs_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [2]> outputs_pad_0 = const()[name = tensor<string, []>("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> outputs_dilations_0 = const()[name = tensor<string, []>("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> outputs_groups_0 = const()[name = tensor<string, []>("outputs_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [64, 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_quantized, x = waveform)[name = tensor<string, []>("outputs")];
+            tensor<fp32, [64, 80, 15975]> input_1 = abs(x = outputs)[name = tensor<string, []>("input_1")];
+            tensor<int32, [1]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_3_ceil_mode_0 = const()[name = tensor<string, []>("input_3_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_3")];
+            tensor<fp32, [64, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor<string, []>("input_5")];
+            tensor<fp32, [64, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor<string, []>("input_7")];
+            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [64, 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_quantized, x = input_7)[name = tensor<string, []>("input_9")];
+            tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_136 = const()[name = tensor<string, []>("op_136"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_11_ceil_mode_0 = const()[name = tensor<string, []>("input_11_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_11")];
+            tensor<fp32, [64, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor<string, []>("input_13")];
+            tensor<fp32, [64, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor<string, []>("input_15")];
+            tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [64, 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_quantized, x = input_15)[name = tensor<string, []>("input_17")];
+            tensor<int32, [1]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_19_ceil_mode_0 = const()[name = tensor<string, []>("input_19_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_19")];
+            tensor<fp32, [64, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor<string, []>("input_21")];
+            tensor<fp32, [64, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = tensor<string, []>("x")];
+            tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
+            tensor<fp32, [512]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(119296)))];
+            tensor<fp32, [512]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(121408)))];
+            tensor<fp32, [512, 60]> concat_4_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_4_quantized"), quantized_data = tensor<int8, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123520))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154880))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_5_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_5_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(156992))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(222592))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 60]> concat_6_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_6_quantized"), quantized_data = tensor<int8, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(224704))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(255488))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_7_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_7_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(257600))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(323200))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [64, 256]> input_25_lstm_layer_0_lstm_h0_reshaped_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped_quantized"), quantized_data = tensor<int8, [64, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(325312))), scale = tensor<fp32, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(341888))), zero_point = tensor<int8, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(341760)))];
+            tensor<string, []> input_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 60]> transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor<string, []>("transpose_1")];
+            tensor<fp32, [589, 64, 256]> input_25_lstm_layer_0_0, tensor<fp32, [64, 256]> input_25_lstm_layer_0_1, tensor<fp32, [64, 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_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_5_quantized, weight_hh_back = concat_7_quantized, weight_ih = concat_4_quantized, weight_ih_back = concat_6_quantized, x = transpose_4)[name = tensor<string, []>("input_25_lstm_layer_0")];
+            tensor<fp32, [512]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(342208)))];
+            tensor<fp32, [512]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(344320)))];
+            tensor<fp32, [512, 256]> concat_14_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_14_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(346432))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(477568))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_15_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_15_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(479680))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(545280))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 256]> concat_16_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_16_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(547392))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(678528))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_17_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_17_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(680640))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(746240))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<string, []> input_25_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 256]> input_25_lstm_layer_1_0, tensor<fp32, [64, 256]> input_25_lstm_layer_1_1, tensor<fp32, [64, 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_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_15_quantized, weight_hh_back = concat_17_quantized, weight_ih = concat_14_quantized, weight_ih_back = concat_16_quantized, x = input_25_lstm_layer_0_0)[name = tensor<string, []>("input_25_lstm_layer_1")];
+            tensor<fp32, [512]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(748352)))];
+            tensor<fp32, [512]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(750464)))];
+            tensor<fp32, [512, 256]> concat_24_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_24_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(752576))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(883712))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_25_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_25_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(885824))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(951424))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 256]> concat_26_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_26_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(953536))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1084672))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_27_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_27_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1086784))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152384))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<string, []> input_25_lstm_layer_2_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_2_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 256]> input_25_lstm_layer_2_0, tensor<fp32, [64, 256]> input_25_lstm_layer_2_1, tensor<fp32, [64, 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_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_25_quantized, weight_hh_back = concat_27_quantized, weight_ih = concat_24_quantized, weight_ih_back = concat_26_quantized, x = input_25_lstm_layer_1_0)[name = tensor<string, []>("input_25_lstm_layer_2")];
+            tensor<fp32, [512]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1154496)))];
+            tensor<fp32, [512]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1156608)))];
+            tensor<fp32, [512, 256]> concat_34_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_34_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1158720))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1289856))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_35_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_35_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1291968))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1357568))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 256]> concat_36_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_36_quantized"), quantized_data = tensor<int8, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1359680))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1490816))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<fp32, [512, 128]> concat_37_quantized = constexpr_affine_dequantize()[axis = tensor<int32, []>(0), name = tensor<string, []>("concat_37_quantized"), quantized_data = tensor<int8, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1492928))), scale = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1558528))), zero_point = tensor<int8, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(154304)))];
+            tensor<string, []> input_25_batch_first_direction_0 = const()[name = tensor<string, []>("input_25_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_25_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_batch_first_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 256]> input_25_batch_first_0, tensor<fp32, [64, 256]> input_25_batch_first_1, tensor<fp32, [64, 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_lstm_layer_0_lstm_h0_reshaped_quantized, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_quantized, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35_quantized, weight_hh_back = concat_37_quantized, weight_ih = concat_34_quantized, weight_ih_back = concat_36_quantized, x = input_25_lstm_layer_2_0)[name = tensor<string, []>("input_25_batch_first")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp32, [64, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor<string, []>("transpose_0")];
+            tensor<fp32, [64, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, x = input_25)[name = tensor<string, []>("linear_0")];
+            tensor<fp32, []> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [64, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor<string, []>("input_29")];
+            tensor<fp32, [64, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight_quantized, x = input_29)[name = tensor<string, []>("linear_1")];
+            tensor<fp32, []> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [64, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor<string, []>("input_33")];
+            tensor<fp32, [64, 589, 7]> input_1_1 = linear(bias = classifier_bias, weight = classifier_weight_quantized, x = input_33)[name = tensor<string, []>("linear_2")];
+            tensor<int32, []> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, []>(-1)];
+            tensor<fp32, [64, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor<string, []>("op_232_softmax")];
+            tensor<fp32, []> var_232_epsilon_0 = const()[name = tensor<string, []>("op_232_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [64, 589, 7]> output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor<string, []>("op_232")];
+        } -> (output);
+}

segmentation-3.0-b64-w8a16.mlmodelc/weights/weight.bin

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+size 1560640

segmentation-3.0-b64.mlmodelc/analytics/coremldata.bin

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+size 243

segmentation-3.0-b64.mlmodelc/coremldata.bin

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segmentation-3.0-b64.mlmodelc/model.mil

@@ -0,0 +1,135 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios16>(tensor<fp32, [64, 1, 160000]> input) {
+            tensor<fp32, [1]> sincnet_wav_norm1d_bias = const()[name = tensor<string, []>("sincnet_wav_norm1d_bias"), val = tensor<fp32, [1]>([0x1.73505ep-5])];
+            tensor<fp32, [1]> sincnet_wav_norm1d_weight = const()[name = tensor<string, []>("sincnet_wav_norm1d_weight"), val = tensor<fp32, [1]>([0x1.43f862p-7])];
+            tensor<fp32, [80]> sincnet_norm1d_0_bias = const()[name = tensor<string, []>("sincnet_norm1d_0_bias"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp32, [80]> sincnet_norm1d_0_weight = const()[name = tensor<string, []>("sincnet_norm1d_0_weight"), val = tensor<fp32, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(448)))];
+            tensor<fp32, [60]> sincnet_conv1d_1_bias = const()[name = tensor<string, []>("sincnet_conv1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(832)))];
+            tensor<fp32, [60, 80, 5]> sincnet_conv1d_1_weight = const()[name = tensor<string, []>("sincnet_conv1d_1_weight"), val = tensor<fp32, [60, 80, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_bias = const()[name = tensor<string, []>("sincnet_norm1d_1_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97216)))];
+            tensor<fp32, [60]> sincnet_norm1d_1_weight = const()[name = tensor<string, []>("sincnet_norm1d_1_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97536)))];
+            tensor<fp32, [60]> sincnet_conv1d_2_bias = const()[name = tensor<string, []>("sincnet_conv1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(97856)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight = const()[name = tensor<string, []>("sincnet_conv1d_2_weight"), val = tensor<fp32, [60, 60, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(98176)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_bias = const()[name = tensor<string, []>("sincnet_norm1d_2_bias"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170240)))];
+            tensor<fp32, [60]> sincnet_norm1d_2_weight = const()[name = tensor<string, []>("sincnet_norm1d_2_weight"), val = tensor<fp32, [60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170560)))];
+            tensor<fp32, [128]> linear_0_bias = const()[name = tensor<string, []>("linear_0_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(170880)))];
+            tensor<fp32, [128, 256]> linear_0_weight = const()[name = tensor<string, []>("linear_0_weight"), val = tensor<fp32, [128, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(171456)))];
+            tensor<fp32, [128]> linear_1_bias = const()[name = tensor<string, []>("linear_1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(302592)))];
+            tensor<fp32, [128, 128]> linear_1_weight = const()[name = tensor<string, []>("linear_1_weight"), val = tensor<fp32, [128, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(303168)))];
+            tensor<fp32, [7]> classifier_bias = const()[name = tensor<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 = tensor<string, []>("classifier_weight"), val = tensor<fp32, [7, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(368768)))];
+            tensor<fp32, []> var_9 = const()[name = tensor<string, []>("op_9"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, []> var_24 = const()[name = tensor<string, []>("op_24"), val = tensor<fp32, []>(0x1.4f8b58p-17)];
+            tensor<fp32, [64, 1, 160000]> waveform = instance_norm(beta = sincnet_wav_norm1d_bias, epsilon = var_24, gamma = sincnet_wav_norm1d_weight, x = input)[name = tensor<string, []>("waveform")];
+            tensor<fp32, [80, 1, 251]> filters = const()[name = tensor<string, []>("filters"), val = tensor<fp32, [80, 1, 251]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(372416)))];
+            tensor<string, []> outputs_pad_type_0 = const()[name = tensor<string, []>("outputs_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> outputs_strides_0 = const()[name = tensor<string, []>("outputs_strides_0"), val = tensor<int32, [1]>([10])];
+            tensor<int32, [2]> outputs_pad_0 = const()[name = tensor<string, []>("outputs_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> outputs_dilations_0 = const()[name = tensor<string, []>("outputs_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> outputs_groups_0 = const()[name = tensor<string, []>("outputs_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [64, 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 = tensor<string, []>("outputs")];
+            tensor<fp32, [64, 80, 15975]> input_1 = abs(x = outputs)[name = tensor<string, []>("input_1")];
+            tensor<int32, [1]> var_119 = const()[name = tensor<string, []>("op_119"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_120 = const()[name = tensor<string, []>("op_120"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_3_pad_type_0 = const()[name = tensor<string, []>("input_3_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_3_pad_0 = const()[name = tensor<string, []>("input_3_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_3_ceil_mode_0 = const()[name = tensor<string, []>("input_3_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_3")];
+            tensor<fp32, [64, 80, 5325]> input_5 = instance_norm(beta = sincnet_norm1d_0_bias, epsilon = var_24, gamma = sincnet_norm1d_0_weight, x = input_3)[name = tensor<string, []>("input_5")];
+            tensor<fp32, [64, 80, 5325]> input_7 = leaky_relu(alpha = var_9, x = input_5)[name = tensor<string, []>("input_7")];
+            tensor<string, []> input_9_pad_type_0 = const()[name = tensor<string, []>("input_9_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_9_strides_0 = const()[name = tensor<string, []>("input_9_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_9_pad_0 = const()[name = tensor<string, []>("input_9_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_9_dilations_0 = const()[name = tensor<string, []>("input_9_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_9_groups_0 = const()[name = tensor<string, []>("input_9_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_9")];
+            tensor<int32, [1]> var_135 = const()[name = tensor<string, []>("op_135"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_136 = const()[name = tensor<string, []>("op_136"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_11_pad_type_0 = const()[name = tensor<string, []>("input_11_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_11_pad_0 = const()[name = tensor<string, []>("input_11_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_11_ceil_mode_0 = const()[name = tensor<string, []>("input_11_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_11")];
+            tensor<fp32, [64, 60, 1773]> input_13 = instance_norm(beta = sincnet_norm1d_1_bias, epsilon = var_24, gamma = sincnet_norm1d_1_weight, x = input_11)[name = tensor<string, []>("input_13")];
+            tensor<fp32, [64, 60, 1773]> input_15 = leaky_relu(alpha = var_9, x = input_13)[name = tensor<string, []>("input_15")];
+            tensor<string, []> input_17_pad_type_0 = const()[name = tensor<string, []>("input_17_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [1]> input_17_strides_0 = const()[name = tensor<string, []>("input_17_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> input_17_pad_0 = const()[name = tensor<string, []>("input_17_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> input_17_dilations_0 = const()[name = tensor<string, []>("input_17_dilations_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, []> input_17_groups_0 = const()[name = tensor<string, []>("input_17_groups_0"), val = tensor<int32, []>(1)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_17")];
+            tensor<int32, [1]> var_151 = const()[name = tensor<string, []>("op_151"), val = tensor<int32, [1]>([3])];
+            tensor<int32, [1]> var_152 = const()[name = tensor<string, []>("op_152"), val = tensor<int32, [1]>([3])];
+            tensor<string, []> input_19_pad_type_0 = const()[name = tensor<string, []>("input_19_pad_type_0"), val = tensor<string, []>("custom")];
+            tensor<int32, [2]> input_19_pad_0 = const()[name = tensor<string, []>("input_19_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<bool, []> input_19_ceil_mode_0 = const()[name = tensor<string, []>("input_19_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp32, [64, 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 = tensor<string, []>("input_19")];
+            tensor<fp32, [64, 60, 589]> input_21 = instance_norm(beta = sincnet_norm1d_2_bias, epsilon = var_24, gamma = sincnet_norm1d_2_weight, x = input_19)[name = tensor<string, []>("input_21")];
+            tensor<fp32, [64, 60, 589]> x = leaky_relu(alpha = var_9, x = input_21)[name = tensor<string, []>("x")];
+            tensor<int32, [3]> transpose_4_perm_0 = const()[name = tensor<string, []>("transpose_4_perm_0"), val = tensor<int32, [3]>([2, 0, 1])];
+            tensor<fp32, [512]> add_0 = const()[name = tensor<string, []>("add_0"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(452800)))];
+            tensor<fp32, [512]> add_1 = const()[name = tensor<string, []>("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(454912)))];
+            tensor<fp32, [512, 60]> concat_4 = const()[name = tensor<string, []>("concat_4"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(457024)))];
+            tensor<fp32, [512, 128]> concat_5 = const()[name = tensor<string, []>("concat_5"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(579968)))];
+            tensor<fp32, [512, 60]> concat_6 = const()[name = tensor<string, []>("concat_6"), val = tensor<fp32, [512, 60]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(842176)))];
+            tensor<fp32, [512, 128]> concat_7 = const()[name = tensor<string, []>("concat_7"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(965120)))];
+            tensor<fp32, [64, 256]> input_25_lstm_layer_0_lstm_h0_reshaped = const()[name = tensor<string, []>("input_25_lstm_layer_0_lstm_h0_reshaped"), val = tensor<fp32, [64, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1227328)))];
+            tensor<string, []> input_25_lstm_layer_0_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_0_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_0_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_0_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_0_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_0_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 60]> transpose_4 = transpose(perm = transpose_4_perm_0, x = x)[name = tensor<string, []>("transpose_6")];
+            tensor<fp32, [589, 64, 256]> input_25_lstm_layer_0_0, tensor<fp32, [64, 256]> input_25_lstm_layer_0_1, tensor<fp32, [64, 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_h0_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_5, weight_hh_back = concat_7, weight_ih = concat_4, weight_ih_back = concat_6, x = transpose_4)[name = tensor<string, []>("input_25_lstm_layer_0")];
+            tensor<fp32, [512]> add_2 = const()[name = tensor<string, []>("add_2"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1292928)))];
+            tensor<fp32, [512]> add_3 = const()[name = tensor<string, []>("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1295040)))];
+            tensor<fp32, [512, 256]> concat_14 = const()[name = tensor<string, []>("concat_14"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1297152)))];
+            tensor<fp32, [512, 128]> concat_15 = const()[name = tensor<string, []>("concat_15"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1821504)))];
+            tensor<fp32, [512, 256]> concat_16 = const()[name = tensor<string, []>("concat_16"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2083712)))];
+            tensor<fp32, [512, 128]> concat_17 = const()[name = tensor<string, []>("concat_17"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2608064)))];
+            tensor<string, []> input_25_lstm_layer_1_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_1_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_1_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_1_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_1_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_1_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 256]> input_25_lstm_layer_1_0, tensor<fp32, [64, 256]> input_25_lstm_layer_1_1, tensor<fp32, [64, 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_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_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_15, weight_hh_back = concat_17, weight_ih = concat_14, weight_ih_back = concat_16, x = input_25_lstm_layer_0_0)[name = tensor<string, []>("input_25_lstm_layer_1")];
+            tensor<fp32, [512]> add_4 = const()[name = tensor<string, []>("add_4"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2870272)))];
+            tensor<fp32, [512]> add_5 = const()[name = tensor<string, []>("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2872384)))];
+            tensor<fp32, [512, 256]> concat_24 = const()[name = tensor<string, []>("concat_24"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2874496)))];
+            tensor<fp32, [512, 128]> concat_25 = const()[name = tensor<string, []>("concat_25"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3398848)))];
+            tensor<fp32, [512, 256]> concat_26 = const()[name = tensor<string, []>("concat_26"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3661056)))];
+            tensor<fp32, [512, 128]> concat_27 = const()[name = tensor<string, []>("concat_27"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4185408)))];
+            tensor<string, []> input_25_lstm_layer_2_direction_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_lstm_layer_2_output_sequence_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_lstm_layer_2_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_lstm_layer_2_cell_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_lstm_layer_2_activation_0 = const()[name = tensor<string, []>("input_25_lstm_layer_2_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 256]> input_25_lstm_layer_2_0, tensor<fp32, [64, 256]> input_25_lstm_layer_2_1, tensor<fp32, [64, 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_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_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_25, weight_hh_back = concat_27, weight_ih = concat_24, weight_ih_back = concat_26, x = input_25_lstm_layer_1_0)[name = tensor<string, []>("input_25_lstm_layer_2")];
+            tensor<fp32, [512]> add_6 = const()[name = tensor<string, []>("add_6"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4447616)))];
+            tensor<fp32, [512]> add_7 = const()[name = tensor<string, []>("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4449728)))];
+            tensor<fp32, [512, 256]> concat_34 = const()[name = tensor<string, []>("concat_34"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4451840)))];
+            tensor<fp32, [512, 128]> concat_35 = const()[name = tensor<string, []>("concat_35"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4976192)))];
+            tensor<fp32, [512, 256]> concat_36 = const()[name = tensor<string, []>("concat_36"), val = tensor<fp32, [512, 256]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5238400)))];
+            tensor<fp32, [512, 128]> concat_37 = const()[name = tensor<string, []>("concat_37"), val = tensor<fp32, [512, 128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5762752)))];
+            tensor<string, []> input_25_batch_first_direction_0 = const()[name = tensor<string, []>("input_25_batch_first_direction_0"), val = tensor<string, []>("bidirectional")];
+            tensor<bool, []> input_25_batch_first_output_sequence_0 = const()[name = tensor<string, []>("input_25_batch_first_output_sequence_0"), val = tensor<bool, []>(true)];
+            tensor<string, []> input_25_batch_first_recurrent_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_recurrent_activation_0"), val = tensor<string, []>("sigmoid")];
+            tensor<string, []> input_25_batch_first_cell_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_cell_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<string, []> input_25_batch_first_activation_0 = const()[name = tensor<string, []>("input_25_batch_first_activation_0"), val = tensor<string, []>("tanh")];
+            tensor<fp32, [589, 64, 256]> input_25_batch_first_0, tensor<fp32, [64, 256]> input_25_batch_first_1, tensor<fp32, [64, 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_lstm_layer_0_lstm_h0_reshaped, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_35, weight_hh_back = concat_37, weight_ih = concat_34, weight_ih_back = concat_36, x = input_25_lstm_layer_2_0)[name = tensor<string, []>("input_25_batch_first")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = tensor<string, []>("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp32, [64, 589, 256]> input_25 = transpose(perm = input_25_perm_0, x = input_25_batch_first_0)[name = tensor<string, []>("transpose_5")];
+            tensor<fp32, [64, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight, x = input_25)[name = tensor<string, []>("linear_0")];
+            tensor<fp32, []> var_220 = const()[name = tensor<string, []>("op_220"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [64, 589, 128]> input_29 = leaky_relu(alpha = var_220, x = input_27)[name = tensor<string, []>("input_29")];
+            tensor<fp32, [64, 589, 128]> input_31 = linear(bias = linear_1_bias, weight = linear_1_weight, x = input_29)[name = tensor<string, []>("linear_1")];
+            tensor<fp32, []> var_225 = const()[name = tensor<string, []>("op_225"), val = tensor<fp32, []>(0x1.47ae14p-7)];
+            tensor<fp32, [64, 589, 128]> input_33 = leaky_relu(alpha = var_225, x = input_31)[name = tensor<string, []>("input_33")];
+            tensor<fp32, [64, 589, 7]> input_1_1 = linear(bias = classifier_bias, weight = classifier_weight, x = input_33)[name = tensor<string, []>("linear_2")];
+            tensor<int32, []> var_231 = const()[name = tensor<string, []>("op_231"), val = tensor<int32, []>(-1)];
+            tensor<fp32, [64, 589, 7]> var_232_softmax = softmax(axis = var_231, x = input_1_1)[name = tensor<string, []>("op_232_softmax")];
+            tensor<fp32, []> var_232_epsilon_0 = const()[name = tensor<string, []>("op_232_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
+            tensor<fp32, [64, 589, 7]> output = log(epsilon = var_232_epsilon_0, x = var_232_softmax)[name = tensor<string, []>("op_232")];
+        } -> (output);
+}

segmentation-3.0-b64.mlmodelc/weights/weight.bin

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+size 6024960

segmentation-3.0-f16.mlmodelc/analytics/coremldata.bin

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segmentation-3.0-f16.mlmodelc/coremldata.bin

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segmentation-3.0-f16.mlmodelc/model.mil

@@ -0,0 +1,227 @@
+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]}}}})))] {
+            fp32 var_9 = const()[name = string("op_9"), val = fp32(0x1.47ae14p-7)];
+            string input_to_fp16_dtype_0 = const()[name = string("input_to_fp16_dtype_0"), val = string("fp16")];
+            tensor<fp16, [1]> sincnet_wav_norm1d_weight_to_fp16 = const()[name = string("sincnet_wav_norm1d_weight_to_fp16"), val = tensor<fp16, [1]>([0x1.44p-7])];
+            tensor<fp16, [1]> sincnet_wav_norm1d_bias_to_fp16 = const()[name = string("sincnet_wav_norm1d_bias_to_fp16"), val = tensor<fp16, [1]>([0x1.734p-5])];
+            fp16 var_24_to_fp16 = const()[name = string("op_24_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [?, 1, 160000]> input_to_fp16 = cast(dtype = input_to_fp16_dtype_0, x = input)[name = string("cast_19")];
+            tensor<fp16, [?, 1, 160000]> waveform_cast_fp16 = instance_norm(beta = sincnet_wav_norm1d_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_wav_norm1d_weight_to_fp16, x = input_to_fp16)[name = string("waveform_cast_fp16")];
+            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<fp16, [80, 1, 251]> filters_to_fp16 = const()[name = string("filters_to_fp16"), val = tensor<fp16, [80, 1, 251]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [?, 80, 15975]> outputs_cast_fp16 = 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_to_fp16, x = waveform_cast_fp16)[name = string("outputs_cast_fp16")];
+            tensor<fp16, [?, 80, 15975]> input_1_cast_fp16 = abs(x = outputs_cast_fp16)[name = string("input_1_cast_fp16")];
+            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<fp16, [?, 80, 5325]> input_3_cast_fp16 = 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_cast_fp16)[name = string("input_3_cast_fp16")];
+            tensor<fp16, [80]> sincnet_norm1d_0_weight_to_fp16 = const()[name = string("sincnet_norm1d_0_weight_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40320)))];
+            tensor<fp16, [80]> sincnet_norm1d_0_bias_to_fp16 = const()[name = string("sincnet_norm1d_0_bias_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40576)))];
+            tensor<fp16, [?, 80, 5325]> input_5_cast_fp16 = instance_norm(beta = sincnet_norm1d_0_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_0_weight_to_fp16, x = input_3_cast_fp16)[name = string("input_5_cast_fp16")];
+            tensor<fp16, [?, 80, 5325]> input_7_cast_fp16 = leaky_relu(alpha = var_9, 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("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<fp16, [60, 80, 5]> sincnet_conv1d_1_weight_to_fp16 = const()[name = string("sincnet_conv1d_1_weight_to_fp16"), val = tensor<fp16, [60, 80, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(40832)))];
+            tensor<fp16, [60]> sincnet_conv1d_1_bias_to_fp16 = const()[name = string("sincnet_conv1d_1_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(88896)))];
+            tensor<fp16, [?, 60, 5321]> input_9_cast_fp16 = conv(bias = sincnet_conv1d_1_bias_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 = sincnet_conv1d_1_weight_to_fp16, x = input_7_cast_fp16)[name = string("input_9_cast_fp16")];
+            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<fp16, [?, 60, 1773]> input_11_cast_fp16 = 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_cast_fp16)[name = string("input_11_cast_fp16")];
+            tensor<fp16, [60]> sincnet_norm1d_1_weight_to_fp16 = const()[name = string("sincnet_norm1d_1_weight_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89088)))];
+            tensor<fp16, [60]> sincnet_norm1d_1_bias_to_fp16 = const()[name = string("sincnet_norm1d_1_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89280)))];
+            tensor<fp16, [?, 60, 1773]> input_13_cast_fp16 = instance_norm(beta = sincnet_norm1d_1_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_1_weight_to_fp16, x = input_11_cast_fp16)[name = string("input_13_cast_fp16")];
+            tensor<fp16, [?, 60, 1773]> input_15_cast_fp16 = leaky_relu(alpha = var_9, x = input_13_cast_fp16)[name = string("input_15_cast_fp16")];
+            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<fp16, [60, 60, 5]> sincnet_conv1d_2_weight_to_fp16 = const()[name = string("sincnet_conv1d_2_weight_to_fp16"), val = tensor<fp16, [60, 60, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89472)))];
+            tensor<fp16, [60]> sincnet_conv1d_2_bias_to_fp16 = const()[name = string("sincnet_conv1d_2_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568)))];
+            tensor<fp16, [?, 60, 1769]> input_17_cast_fp16 = conv(bias = sincnet_conv1d_2_bias_to_fp16, 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_to_fp16, x = input_15_cast_fp16)[name = string("input_17_cast_fp16")];
+            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<fp16, [?, 60, 589]> input_19_cast_fp16 = 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_cast_fp16)[name = string("input_19_cast_fp16")];
+            tensor<fp16, [60]> sincnet_norm1d_2_weight_to_fp16 = const()[name = string("sincnet_norm1d_2_weight_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125760)))];
+            tensor<fp16, [60]> sincnet_norm1d_2_bias_to_fp16 = const()[name = string("sincnet_norm1d_2_bias_to_fp16"), val = tensor<fp16, [60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125952)))];
+            tensor<fp16, [?, 60, 589]> input_21_cast_fp16 = instance_norm(beta = sincnet_norm1d_2_bias_to_fp16, epsilon = var_24_to_fp16, gamma = sincnet_norm1d_2_weight_to_fp16, x = input_19_cast_fp16)[name = string("input_21_cast_fp16")];
+            tensor<fp16, [?, 60, 589]> x_cast_fp16 = leaky_relu(alpha = var_9, x = input_21_cast_fp16)[name = string("x_cast_fp16")];
+            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<fp16, [?, 589, 60]> input_23_cast_fp16 = transpose(perm = var_163, x = x_cast_fp16)[name = string("transpose_6")];
+            tensor<int32, [3]> var_207_shape_cast_fp16 = shape(x = input_23_cast_fp16)[name = string("op_207_shape_cast_fp16")];
+            int32 gather_0_axis_0 = const()[name = string("gather_0_axis_0"), val = int32(0)];
+            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)];
+            string var_207_shape_cast_fp16_to_int16_dtype_0 = const()[name = string("op_207_shape_cast_fp16_to_int16_dtype_0"), val = string("int16")];
+            uint16 gather_0_indices_0_to_uint16 = const()[name = string("gather_0_indices_0_to_uint16"), val = uint16(0)];
+            tensor<int16, [3]> var_207_shape_cast_fp16_to_int16 = cast(dtype = var_207_shape_cast_fp16_to_int16_dtype_0, x = var_207_shape_cast_fp16)[name = string("cast_18")];
+            int16 gather_0_cast_uint16 = gather(axis = gather_0_axis_0, batch_dims = gather_0_batch_dims_0, indices = gather_0_indices_0_to_uint16, validate_indices = gather_0_validate_indices_0, x = var_207_shape_cast_fp16_to_int16)[name = string("gather_0_cast_uint16")];
+            string gather_0_cast_uint16_to_int32_dtype_0 = const()[name = string("gather_0_cast_uint16_to_int32_dtype_0"), val = string("int32")];
+            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)];
+            int32 gather_0_cast_uint16_to_int32 = cast(dtype = gather_0_cast_uint16_to_int32_dtype_0, x = gather_0_cast_uint16)[name = string("cast_17")];
+            tensor<int32, [3]> concat_0 = concat(axis = concat_0_axis_0, interleave = concat_0_interleave_0, values = (var_173, gather_0_cast_uint16_to_int32, var_172))[name = string("concat_0")];
+            fp16 hx_1_value_0_to_fp16 = const()[name = string("hx_1_value_0_to_fp16"), val = fp16(0x0p+0)];
+            tensor<fp16, [8, ?, 128]> hx_1_cast_fp16 = fill(shape = concat_0, value = hx_1_value_0_to_fp16)[name = string("hx_1_cast_fp16")];
+            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<fp16, [2, ?, 128]> split_0_cast_fp16_0, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_1, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_2, tensor<fp16, [2, ?, 128]> split_0_cast_fp16_3 = split(axis = split_0_axis_0, num_splits = split_0_num_splits_0, x = hx_1_cast_fp16)[name = string("split_0_cast_fp16")];
+            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<fp16, [2, ?, 128]> split_1_cast_fp16_0, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_1, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_2, tensor<fp16, [2, ?, 128]> split_1_cast_fp16_3 = split(axis = split_1_axis_0, num_splits = split_1_num_splits_0, x = hx_1_cast_fp16)[name = string("split_1_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_10_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_10_cast_fp16_1 = split(axis = split_10_axis_0, split_sizes = split_10_split_sizes_0, x = split_0_cast_fp16_0)[name = string("split_10_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_10_cast_fp16 = concat(axis = concat_10_axis_0, interleave = concat_10_interleave_0, values = (split_10_cast_fp16_0, split_10_cast_fp16_1))[name = string("concat_10_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_h0_reshaped_axes_0, x = concat_10_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_11_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_11_cast_fp16_1 = split(axis = split_11_axis_0, split_sizes = split_11_split_sizes_0, x = split_1_cast_fp16_0)[name = string("split_11_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_11_cast_fp16 = concat(axis = concat_11_axis_0, interleave = concat_11_interleave_0, values = (split_11_cast_fp16_0, split_11_cast_fp16_1))[name = string("concat_11_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_0_lstm_c0_reshaped_axes_0, x = concat_11_cast_fp16)[name = string("input_25_lstm_layer_0_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 60]> concat_6_to_fp16 = const()[name = string("concat_6_to_fp16"), val = tensor<fp16, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126144)))];
+            tensor<fp16, [512, 128]> concat_7_to_fp16 = const()[name = string("concat_7_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187648)))];
+            tensor<fp16, [512]> add_0_to_fp16 = const()[name = string("add_0_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(318784)))];
+            tensor<fp16, [512, 60]> concat_8_to_fp16 = const()[name = string("concat_8_to_fp16"), val = tensor<fp16, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(319872)))];
+            tensor<fp16, [512, 128]> concat_9_to_fp16 = const()[name = string("concat_9_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(381376)))];
+            tensor<fp16, [512]> add_1_to_fp16 = const()[name = string("add_1_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(512512)))];
+            tensor<fp16, [589, ?, 60]> input_23_batch_first_transpose_cast_fp16 = transpose(perm = input_23_batch_first_transpose_perm_0, x = input_23_cast_fp16)[name = string("transpose_5")];
+            tensor<fp16, [589, ?, 256]> input_25_lstm_layer_0_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_0_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_0_cast_fp16_2 = lstm(activation = input_25_lstm_layer_0_activation_0, bias = add_0_to_fp16, bias_back = add_1_to_fp16, 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_cast_fp16, initial_h = input_25_lstm_layer_0_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_0_output_sequence_0, recurrent_activation = input_25_lstm_layer_0_recurrent_activation_0, weight_hh = concat_7_to_fp16, weight_hh_back = concat_9_to_fp16, weight_ih = concat_6_to_fp16, weight_ih_back = concat_8_to_fp16, x = input_23_batch_first_transpose_cast_fp16)[name = string("input_25_lstm_layer_0_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_20_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_20_cast_fp16_1 = split(axis = split_20_axis_0, split_sizes = split_20_split_sizes_0, x = split_0_cast_fp16_1)[name = string("split_20_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_20_cast_fp16 = concat(axis = concat_20_axis_0, interleave = concat_20_interleave_0, values = (split_20_cast_fp16_0, split_20_cast_fp16_1))[name = string("concat_20_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_h0_reshaped_axes_0, x = concat_20_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_21_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_21_cast_fp16_1 = split(axis = split_21_axis_0, split_sizes = split_21_split_sizes_0, x = split_1_cast_fp16_1)[name = string("split_21_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_21_cast_fp16 = concat(axis = concat_21_axis_0, interleave = concat_21_interleave_0, values = (split_21_cast_fp16_0, split_21_cast_fp16_1))[name = string("concat_21_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_1_lstm_c0_reshaped_axes_0, x = concat_21_cast_fp16)[name = string("input_25_lstm_layer_1_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 256]> concat_16_to_fp16 = const()[name = string("concat_16_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513600)))];
+            tensor<fp16, [512, 128]> concat_17_to_fp16 = const()[name = string("concat_17_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(775808)))];
+            tensor<fp16, [512]> add_2_to_fp16 = const()[name = string("add_2_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906944)))];
+            tensor<fp16, [512, 256]> concat_18_to_fp16 = const()[name = string("concat_18_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(908032)))];
+            tensor<fp16, [512, 128]> concat_19_to_fp16 = const()[name = string("concat_19_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1170240)))];
+            tensor<fp16, [512]> add_3_to_fp16 = const()[name = string("add_3_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1301376)))];
+            tensor<fp16, [589, ?, 256]> input_25_lstm_layer_1_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_1_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_1_cast_fp16_2 = lstm(activation = input_25_lstm_layer_1_activation_0, bias = add_2_to_fp16, bias_back = add_3_to_fp16, 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_cast_fp16, initial_h = input_25_lstm_layer_1_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_1_output_sequence_0, recurrent_activation = input_25_lstm_layer_1_recurrent_activation_0, weight_hh = concat_17_to_fp16, weight_hh_back = concat_19_to_fp16, weight_ih = concat_16_to_fp16, weight_ih_back = concat_18_to_fp16, x = input_25_lstm_layer_0_cast_fp16_0)[name = string("input_25_lstm_layer_1_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_30_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_30_cast_fp16_1 = split(axis = split_30_axis_0, split_sizes = split_30_split_sizes_0, x = split_0_cast_fp16_2)[name = string("split_30_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_30_cast_fp16 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (split_30_cast_fp16_0, split_30_cast_fp16_1))[name = string("concat_30_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_h0_reshaped_axes_0, x = concat_30_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_31_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_31_cast_fp16_1 = split(axis = split_31_axis_0, split_sizes = split_31_split_sizes_0, x = split_1_cast_fp16_2)[name = string("split_31_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_31_cast_fp16 = concat(axis = concat_31_axis_0, interleave = concat_31_interleave_0, values = (split_31_cast_fp16_0, split_31_cast_fp16_1))[name = string("concat_31_cast_fp16")];
+            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<fp16, [?, 256]> input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_lstm_layer_2_lstm_c0_reshaped_axes_0, x = concat_31_cast_fp16)[name = string("input_25_lstm_layer_2_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 256]> concat_26_to_fp16 = const()[name = string("concat_26_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1302464)))];
+            tensor<fp16, [512, 128]> concat_27_to_fp16 = const()[name = string("concat_27_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1564672)))];
+            tensor<fp16, [512]> add_4_to_fp16 = const()[name = string("add_4_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1695808)))];
+            tensor<fp16, [512, 256]> concat_28_to_fp16 = const()[name = string("concat_28_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1696896)))];
+            tensor<fp16, [512, 128]> concat_29_to_fp16 = const()[name = string("concat_29_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1959104)))];
+            tensor<fp16, [512]> add_5_to_fp16 = const()[name = string("add_5_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2090240)))];
+            tensor<fp16, [589, ?, 256]> input_25_lstm_layer_2_cast_fp16_0, tensor<fp16, [?, 256]> input_25_lstm_layer_2_cast_fp16_1, tensor<fp16, [?, 256]> input_25_lstm_layer_2_cast_fp16_2 = lstm(activation = input_25_lstm_layer_2_activation_0, bias = add_4_to_fp16, bias_back = add_5_to_fp16, 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_cast_fp16, initial_h = input_25_lstm_layer_2_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_lstm_layer_2_output_sequence_0, recurrent_activation = input_25_lstm_layer_2_recurrent_activation_0, weight_hh = concat_27_to_fp16, weight_hh_back = concat_29_to_fp16, weight_ih = concat_26_to_fp16, weight_ih_back = concat_28_to_fp16, x = input_25_lstm_layer_1_cast_fp16_0)[name = string("input_25_lstm_layer_2_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_40_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_40_cast_fp16_1 = split(axis = split_40_axis_0, split_sizes = split_40_split_sizes_0, x = split_0_cast_fp16_3)[name = string("split_40_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_40_cast_fp16 = concat(axis = concat_40_axis_0, interleave = concat_40_interleave_0, values = (split_40_cast_fp16_0, split_40_cast_fp16_1))[name = string("concat_40_cast_fp16")];
+            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<fp16, [?, 256]> input_25_batch_first_lstm_h0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_h0_reshaped_axes_0, x = concat_40_cast_fp16)[name = string("input_25_batch_first_lstm_h0_reshaped_cast_fp16")];
+            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<fp16, [1, ?, 128]> split_41_cast_fp16_0, tensor<fp16, [1, ?, 128]> split_41_cast_fp16_1 = split(axis = split_41_axis_0, split_sizes = split_41_split_sizes_0, x = split_1_cast_fp16_3)[name = string("split_41_cast_fp16")];
+            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<fp16, [1, ?, 256]> concat_41_cast_fp16 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (split_41_cast_fp16_0, split_41_cast_fp16_1))[name = string("concat_41_cast_fp16")];
+            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<fp16, [?, 256]> input_25_batch_first_lstm_c0_reshaped_cast_fp16 = squeeze(axes = input_25_batch_first_lstm_c0_reshaped_axes_0, x = concat_41_cast_fp16)[name = string("input_25_batch_first_lstm_c0_reshaped_cast_fp16")];
+            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<fp16, [512, 256]> concat_36_to_fp16 = const()[name = string("concat_36_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2091328)))];
+            tensor<fp16, [512, 128]> concat_37_to_fp16 = const()[name = string("concat_37_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2353536)))];
+            tensor<fp16, [512]> add_6_to_fp16 = const()[name = string("add_6_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2484672)))];
+            tensor<fp16, [512, 256]> concat_38_to_fp16 = const()[name = string("concat_38_to_fp16"), val = tensor<fp16, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2485760)))];
+            tensor<fp16, [512, 128]> concat_39_to_fp16 = const()[name = string("concat_39_to_fp16"), val = tensor<fp16, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2747968)))];
+            tensor<fp16, [512]> add_7_to_fp16 = const()[name = string("add_7_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2879104)))];
+            tensor<fp16, [589, ?, 256]> input_25_batch_first_cast_fp16_0, tensor<fp16, [?, 256]> input_25_batch_first_cast_fp16_1, tensor<fp16, [?, 256]> input_25_batch_first_cast_fp16_2 = lstm(activation = input_25_batch_first_activation_0, bias = add_6_to_fp16, bias_back = add_7_to_fp16, 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_cast_fp16, initial_h = input_25_batch_first_lstm_h0_reshaped_cast_fp16, output_sequence = input_25_batch_first_output_sequence_0, recurrent_activation = input_25_batch_first_recurrent_activation_0, weight_hh = concat_37_to_fp16, weight_hh_back = concat_39_to_fp16, weight_ih = concat_36_to_fp16, weight_ih_back = concat_38_to_fp16, x = input_25_lstm_layer_2_cast_fp16_0)[name = string("input_25_batch_first_cast_fp16")];
+            tensor<int32, [3]> input_25_perm_0 = const()[name = string("input_25_perm_0"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<fp16, [128, 256]> linear_0_weight_to_fp16 = const()[name = string("linear_0_weight_to_fp16"), val = tensor<fp16, [128, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2880192)))];
+            tensor<fp16, [128]> linear_0_bias_to_fp16 = const()[name = string("linear_0_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2945792)))];
+            tensor<fp16, [?, 589, 256]> input_25_cast_fp16 = transpose(perm = input_25_perm_0, x = input_25_batch_first_cast_fp16_0)[name = string("transpose_4")];
+            tensor<fp16, [?, 589, 128]> linear_0_cast_fp16 = linear(bias = linear_0_bias_to_fp16, weight = linear_0_weight_to_fp16, x = input_25_cast_fp16)[name = string("linear_0_cast_fp16")];
+            fp32 var_220 = const()[name = string("op_220"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp16, [?, 589, 128]> input_29_cast_fp16 = leaky_relu(alpha = var_220, x = linear_0_cast_fp16)[name = string("input_29_cast_fp16")];
+            tensor<fp16, [128, 128]> linear_1_weight_to_fp16 = const()[name = string("linear_1_weight_to_fp16"), val = tensor<fp16, [128, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2946112)))];
+            tensor<fp16, [128]> linear_1_bias_to_fp16 = const()[name = string("linear_1_bias_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2978944)))];
+            tensor<fp16, [?, 589, 128]> linear_1_cast_fp16 = linear(bias = linear_1_bias_to_fp16, weight = linear_1_weight_to_fp16, x = input_29_cast_fp16)[name = string("linear_1_cast_fp16")];
+            fp32 var_225 = const()[name = string("op_225"), val = fp32(0x1.47ae14p-7)];
+            tensor<fp16, [?, 589, 128]> input_33_cast_fp16 = leaky_relu(alpha = var_225, x = linear_1_cast_fp16)[name = string("input_33_cast_fp16")];
+            tensor<fp16, [7, 128]> classifier_weight_to_fp16 = const()[name = string("classifier_weight_to_fp16"), val = tensor<fp16, [7, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2979264)))];
+            tensor<fp16, [7]> classifier_bias_to_fp16 = const()[name = string("classifier_bias_to_fp16"), val = tensor<fp16, [7]>([-0x1.01p+0, 0x1.67cp-2, 0x1.3d8p-1, 0x1.c8cp-2, -0x1.444p-2, -0x1.59p-1, -0x1.8fcp-2])];
+            tensor<fp16, [?, 589, 7]> linear_2_cast_fp16 = linear(bias = classifier_bias_to_fp16, weight = classifier_weight_to_fp16, x = input_33_cast_fp16)[name = string("linear_2_cast_fp16")];
+            int32 var_231 = const()[name = string("op_231"), val = int32(-1)];
+            tensor<fp16, [?, 589, 7]> var_232_softmax_cast_fp16 = softmax(axis = var_231, x = linear_2_cast_fp16)[name = string("op_232_softmax_cast_fp16")];
+            fp32 var_232_epsilon_0 = const()[name = string("op_232_epsilon_0"), val = fp32(0x1p-149)];
+            tensor<fp16, [?, 589, 7]> var_232_cast_fp16 = log(epsilon = var_232_epsilon_0, x = var_232_softmax_cast_fp16)[name = string("op_232_cast_fp16")];
+            string var_232_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_232_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
+            tensor<fp32, [?, 589, 7]> output = cast(dtype = var_232_cast_fp16_to_fp32_dtype_0, x = var_232_cast_fp16)[name = string("cast_16")];
+        } -> (output);
+}

segmentation-3.0-f16.mlmodelc/weights/weight.bin

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segmentation-3.0-w8a16.mlmodelc/analytics/coremldata.bin

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segmentation-3.0-w8a16.mlmodelc/coremldata.bin

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+size 664

segmentation-3.0-w8a16.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_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [60, 80, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152))), scale = tensor<fp32, [60, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(25216))))[name = string("sincnet_conv1d_1_weight_quantized")];
+            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(25536)))];
+            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(25856)))];
+            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(26176)))];
+            tensor<fp32, [60, 60, 5]> sincnet_conv1d_2_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [60, 60, 5]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26496))), scale = tensor<fp32, [60, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44608))))[name = string("sincnet_conv1d_2_weight_quantized")];
+            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(44928)))];
+            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(45248)))];
+            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(45568)))];
+            tensor<fp32, [128, 256]> linear_0_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(46144))), scale = tensor<fp32, [128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78976))))[name = string("linear_0_weight_quantized")];
+            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(79552)))];
+            tensor<fp32, [128, 128]> linear_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80128))), scale = tensor<fp32, [128, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(96576))))[name = string("linear_1_weight_quantized")];
+            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(97152)))];
+            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_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [80, 1, 251]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(100800))), scale = tensor<fp32, [80, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120960))))[name = string("filters_quantized")];
+            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_quantized, 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_quantized, 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_quantized, 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_2")];
+            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_0 = const()[name = string("gather_0_axis_0"), val = int32(0)];
+            int32 gather_0 = gather(axis = gather_0_axis_0, 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(121344)))];
+            tensor<fp32, [512]> add_1 = const()[name = string("add_1"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(123456)))];
+            tensor<fp32, [512, 60]> concat_6_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(125568))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(156352))))[name = string("concat_6_quantized")];
+            tensor<fp32, [512, 128]> concat_7_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158464))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224064))))[name = string("concat_7_quantized")];
+            tensor<fp32, [512, 60]> concat_8_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 60]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(226176))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(256960))))[name = string("concat_8_quantized")];
+            tensor<fp32, [512, 128]> concat_9_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(259072))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(324672))))[name = string("concat_9_quantized")];
+            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_1")];
+            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_quantized, weight_hh_back = concat_9_quantized, weight_ih = concat_6_quantized, weight_ih_back = concat_8_quantized, 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(326784)))];
+            tensor<fp32, [512]> add_3 = const()[name = string("add_3"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(328896)))];
+            tensor<fp32, [512, 256]> concat_16_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(331008))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(462144))))[name = string("concat_16_quantized")];
+            tensor<fp32, [512, 128]> concat_17_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(464256))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(529856))))[name = string("concat_17_quantized")];
+            tensor<fp32, [512, 256]> concat_18_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531968))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(663104))))[name = string("concat_18_quantized")];
+            tensor<fp32, [512, 128]> concat_19_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(665216))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(730816))))[name = string("concat_19_quantized")];
+            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_quantized, weight_hh_back = concat_19_quantized, weight_ih = concat_16_quantized, weight_ih_back = concat_18_quantized, 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(732928)))];
+            tensor<fp32, [512]> add_5 = const()[name = string("add_5"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(735040)))];
+            tensor<fp32, [512, 256]> concat_26_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(737152))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(868288))))[name = string("concat_26_quantized")];
+            tensor<fp32, [512, 128]> concat_27_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(870400))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936000))))[name = string("concat_27_quantized")];
+            tensor<fp32, [512, 256]> concat_28_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(938112))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069248))))[name = string("concat_28_quantized")];
+            tensor<fp32, [512, 128]> concat_29_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1071360))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1136960))))[name = string("concat_29_quantized")];
+            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_quantized, weight_hh_back = concat_29_quantized, weight_ih = concat_26_quantized, weight_ih_back = concat_28_quantized, 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(1139072)))];
+            tensor<fp32, [512]> add_7 = const()[name = string("add_7"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1141184)))];
+            tensor<fp32, [512, 256]> concat_36_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1143296))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274432))))[name = string("concat_36_quantized")];
+            tensor<fp32, [512, 128]> concat_37_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1276544))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1342144))))[name = string("concat_37_quantized")];
+            tensor<fp32, [512, 256]> concat_38_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1344256))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475392))))[name = string("concat_38_quantized")];
+            tensor<fp32, [512, 128]> concat_39_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [512, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1477504))), scale = tensor<fp32, [512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1543104))))[name = string("concat_39_quantized")];
+            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_quantized, weight_hh_back = concat_39_quantized, weight_ih = concat_36_quantized, weight_ih_back = concat_38_quantized, 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_0")];
+            tensor<fp32, [?, 589, 128]> input_27 = linear(bias = linear_0_bias, weight = linear_0_weight_quantized, 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_quantized, 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-w8a16.mlmodelc/weights/weight.bin

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wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/analytics/coremldata.bin

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+size 243

wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/coremldata.bin

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+size 403

wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/model.mil

@@ -0,0 +1,427 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 12040, 80]> fbank, tensor<fp32, [348, 589]> masks) {
+            tensor<fp32, [256, 5120]> p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor<fp32, [256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")];
+            tensor<fp32, [256]> p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))];
+            tensor<int32, [3]> const_0 = const()[name = string("const_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 80, 12040]> permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")];
+            tensor<fp32, [1, 1, 80, 12040]> unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")];
+            string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_201 = const()[name = string("const_201"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))];
+            tensor<fp32, [32]> const_202 = const()[name = string("const_202"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")];
+            tensor<fp32, [1, 32, 80, 12040]> relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")];
+            string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_203_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")];
+            tensor<fp32, [32]> const_204 = const()[name = string("const_204"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")];
+            string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_205_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")];
+            tensor<fp32, [32]> const_206 = const()[name = string("const_206"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")];
+            tensor<fp32, [1, 32, 80, 12040]> add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_2 = relu(x = add)[name = string("relu_2")];
+            string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_207_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")];
+            tensor<fp32, [32]> const_208 = const()[name = string("const_208"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")];
+            string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_209_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")];
+            tensor<fp32, [32]> const_210 = const()[name = string("const_210"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")];
+            tensor<fp32, [1, 32, 80, 12040]> add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_4 = relu(x = add_1)[name = string("relu_4")];
+            string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_211_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")];
+            tensor<fp32, [32]> const_212 = const()[name = string("const_212"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")];
+            string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_213_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")];
+            tensor<fp32, [32]> const_214 = const()[name = string("const_214"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")];
+            tensor<fp32, [1, 32, 80, 12040]> add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_6 = relu(x = add_2)[name = string("relu_6")];
+            string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_215_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")];
+            tensor<fp32, [64]> const_216 = const()[name = string("const_216"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")];
+            string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_217_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")];
+            tensor<fp32, [64]> const_218 = const()[name = string("const_218"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")];
+            string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_219 = const()[name = string("const_219"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))];
+            tensor<fp32, [64]> const_220 = const()[name = string("const_220"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")];
+            tensor<fp32, [1, 64, 40, 6020]> add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_8 = relu(x = add_3)[name = string("relu_8")];
+            string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_221_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")];
+            tensor<fp32, [64]> const_222 = const()[name = string("const_222"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")];
+            string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_223_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")];
+            tensor<fp32, [64]> const_224 = const()[name = string("const_224"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")];
+            tensor<fp32, [1, 64, 40, 6020]> add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_10 = relu(x = add_4)[name = string("relu_10")];
+            string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_225_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")];
+            tensor<fp32, [64]> const_226 = const()[name = string("const_226"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")];
+            string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_227_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")];
+            tensor<fp32, [64]> const_228 = const()[name = string("const_228"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")];
+            tensor<fp32, [1, 64, 40, 6020]> add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_12 = relu(x = add_5)[name = string("relu_12")];
+            string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_229_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")];
+            tensor<fp32, [64]> const_230 = const()[name = string("const_230"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")];
+            string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_231_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")];
+            tensor<fp32, [64]> const_232 = const()[name = string("const_232"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")];
+            tensor<fp32, [1, 64, 40, 6020]> add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_14 = relu(x = add_6)[name = string("relu_14")];
+            string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_233_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")];
+            tensor<fp32, [128]> const_234 = const()[name = string("const_234"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")];
+            string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_235_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")];
+            tensor<fp32, [128]> const_236 = const()[name = string("const_236"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")];
+            string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_237_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")];
+            tensor<fp32, [128]> const_238 = const()[name = string("const_238"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")];
+            tensor<fp32, [1, 128, 20, 3010]> add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_16 = relu(x = add_7)[name = string("relu_16")];
+            string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_239_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")];
+            tensor<fp32, [128]> const_240 = const()[name = string("const_240"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")];
+            string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_241_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")];
+            tensor<fp32, [128]> const_242 = const()[name = string("const_242"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")];
+            tensor<fp32, [1, 128, 20, 3010]> add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_18 = relu(x = add_8)[name = string("relu_18")];
+            string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_243_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")];
+            tensor<fp32, [128]> const_244 = const()[name = string("const_244"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")];
+            string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_245_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")];
+            tensor<fp32, [128]> const_246 = const()[name = string("const_246"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")];
+            tensor<fp32, [1, 128, 20, 3010]> add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_20 = relu(x = add_9)[name = string("relu_20")];
+            string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_247_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")];
+            tensor<fp32, [128]> const_248 = const()[name = string("const_248"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")];
+            string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_249_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")];
+            tensor<fp32, [128]> const_250 = const()[name = string("const_250"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")];
+            tensor<fp32, [1, 128, 20, 3010]> add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_22 = relu(x = add_10)[name = string("relu_22")];
+            string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_251_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")];
+            tensor<fp32, [128]> const_252 = const()[name = string("const_252"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")];
+            string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_253_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")];
+            tensor<fp32, [128]> const_254 = const()[name = string("const_254"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")];
+            tensor<fp32, [1, 128, 20, 3010]> add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_24 = relu(x = add_11)[name = string("relu_24")];
+            string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_255_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")];
+            tensor<fp32, [128]> const_256 = const()[name = string("const_256"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")];
+            string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_257_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")];
+            tensor<fp32, [128]> const_258 = const()[name = string("const_258"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")];
+            tensor<fp32, [1, 128, 20, 3010]> add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_26 = relu(x = add_12)[name = string("relu_26")];
+            string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_259_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")];
+            tensor<fp32, [256]> const_260 = const()[name = string("const_260"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")];
+            string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_261_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")];
+            tensor<fp32, [256]> const_262 = const()[name = string("const_262"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")];
+            string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_263_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")];
+            tensor<fp32, [256]> const_264 = const()[name = string("const_264"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")];
+            tensor<fp32, [1, 256, 10, 1505]> add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_28 = relu(x = add_13)[name = string("relu_28")];
+            string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_265_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")];
+            tensor<fp32, [256]> const_266 = const()[name = string("const_266"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")];
+            string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_267_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")];
+            tensor<fp32, [256]> const_268 = const()[name = string("const_268"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")];
+            tensor<fp32, [1, 256, 10, 1505]> add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_30 = relu(x = add_14)[name = string("relu_30")];
+            string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_269_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")];
+            tensor<fp32, [256]> const_270 = const()[name = string("const_270"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")];
+            string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_271_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")];
+            tensor<fp32, [256]> const_272 = const()[name = string("const_272"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")];
+            tensor<fp32, [1, 256, 10, 1505]> add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_32 = relu(x = add_15)[name = string("relu_32")];
+            tensor<int32, [3]> const_179 = const()[name = string("const_179"), val = tensor<int32, [3]>([1, 2560, -1])];
+            tensor<fp32, [1, 2560, 1505]> view = reshape(shape = const_179, x = relu_32)[name = string("view")];
+            tensor<int32, [1]> squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [2560, 1505]> squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")];
+            int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)];
+            bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)];
+            tensor<int32, [14500]> select_0 = const()[name = string("select_0"), val = tensor<int32, [14500]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))];
+            int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)];
+            tensor<fp32, [2560, 14500]> index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")];
+            tensor<int32, [3]> const_182 = const()[name = string("const_182"), val = tensor<int32, [3]>([2560, 116, 125])];
+            tensor<fp32, [2560, 116, 125]> view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")];
+            tensor<int32, [3]> const_183 = const()[name = string("const_183"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, [3]> tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor<int32, [3]>([3, 1, 1])];
+            tensor<fp32, [116, 2560, 125]> permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")];
+            tensor<fp32, [348, 2560, 125]> tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")];
+            tensor<int32, [4]> concat_0 = const()[name = string("concat_0"), val = tensor<int32, [4]>([3, 116, 2560, 125])];
+            tensor<fp32, [3, 116, 2560, 125]> reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")];
+            tensor<int32, [4]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [4]>([1, 0, 2, 3])];
+            tensor<int32, [3]> concat_1 = const()[name = string("concat_1"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [116, 3, 2560, 125]> transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")];
+            tensor<fp32, [348, 2560, 125]> repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")];
+            tensor<int32, [1]> unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [348, 1, 589]> unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [348, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[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, [348, 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]> upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [348, 1, 125]> upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")];
+            tensor<int32, [1]> sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 1]> sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")];
+            fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)];
+            tensor<bool, [348, 1]> gt = greater(x = sum_1, y = const_189)[name = string("gt")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [348, 1]> fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [348, 1]> where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")];
+            tensor<fp32, [348, 2560, 125]> mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")];
+            tensor<int32, [1]> sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 2560]> sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")];
+            tensor<fp32, [348, 2560]> div = real_div(x = sum_2, y = where)[name = string("div")];
+            tensor<int32, [1]> unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [348, 2560, 1]> unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")];
+            tensor<fp32, [348, 2560, 125]> sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")];
+            tensor<fp32, [348, 2560, 125]> square = mul(x = sub, y = sub)[name = string("square")];
+            tensor<fp32, [348, 1, 125]> square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")];
+            tensor<int32, [1]> sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 1]> sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")];
+            tensor<fp32, [348, 1]> div_1 = real_div(x = sum_3, y = where)[name = string("div_1")];
+            tensor<fp32, [348, 1]> sub_1 = sub(x = where, y = div_1)[name = string("sub_1")];
+            fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [348, 1]> add_16 = add(x = sub_1, y = const_193)[name = string("add_16")];
+            tensor<fp32, [348, 2560, 125]> mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")];
+            tensor<int32, [1]> sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 2560]> sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")];
+            tensor<fp32, [348, 2560]> div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")];
+            fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [348, 2560]> clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")];
+            tensor<fp32, [348, 2560]> sqrt = sqrt(x = clamp_min)[name = string("sqrt")];
+            int32 const_196 = const()[name = string("const_196"), val = int32(-1)];
+            bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)];
+            tensor<fp32, [348, 5120]> cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")];
+            tensor<fp32, [348, 2560]> zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [348, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6733440))), scale = tensor<fp32, [348, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7624384))))[name = string("zeros_like_quantized")];
+            fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [348, 2560]> full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")];
+            int32 const_198 = const()[name = string("const_198"), val = int32(-1)];
+            bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)];
+            tensor<fp32, [348, 5120]> cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")];
+            fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)];
+            tensor<bool, [348, 1]> le = less_equal(x = sum_1, y = const_199)[name = string("le")];
+            tensor<int32, [2]> const_200 = const()[name = string("const_200"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [348, 5120]> repeat = tile(reps = const_200, x = le)[name = string("repeat")];
+            tensor<fp32, [348, 5120]> where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")];
+            tensor<fp32, [348, 256]> output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-chunk-emb-s12-w116-w8a16.mlmodelc/weights/weight.bin

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+size 7625840

wespeaker-chunk-emb-s12-w116.mlmodelc/analytics/coremldata.bin

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+size 243

wespeaker-chunk-emb-s12-w116.mlmodelc/coremldata.bin

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wespeaker-chunk-emb-s12-w116.mlmodelc/model.mil

@@ -0,0 +1,427 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 12040, 80]> fbank, tensor<fp32, [348, 589]> masks) {
+            tensor<fp32, [256, 5120]> p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [256]> p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))];
+            tensor<int32, [3]> const_0 = const()[name = string("const_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 80, 12040]> permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")];
+            tensor<fp32, [1, 1, 80, 12040]> unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")];
+            string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_201 = const()[name = string("const_201"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))];
+            tensor<fp32, [32]> const_202 = const()[name = string("const_202"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")];
+            tensor<fp32, [1, 32, 80, 12040]> relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")];
+            string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_203 = const()[name = string("const_203"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))];
+            tensor<fp32, [32]> const_204 = const()[name = string("const_204"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")];
+            string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_205 = const()[name = string("const_205"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))];
+            tensor<fp32, [32]> const_206 = const()[name = string("const_206"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")];
+            tensor<fp32, [1, 32, 80, 12040]> add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_2 = relu(x = add)[name = string("relu_2")];
+            string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_207 = const()[name = string("const_207"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))];
+            tensor<fp32, [32]> const_208 = const()[name = string("const_208"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")];
+            string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_209 = const()[name = string("const_209"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))];
+            tensor<fp32, [32]> const_210 = const()[name = string("const_210"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")];
+            tensor<fp32, [1, 32, 80, 12040]> add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_4 = relu(x = add_1)[name = string("relu_4")];
+            string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_211 = const()[name = string("const_211"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))];
+            tensor<fp32, [32]> const_212 = const()[name = string("const_212"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")];
+            string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_213 = const()[name = string("const_213"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))];
+            tensor<fp32, [32]> const_214 = const()[name = string("const_214"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))];
+            tensor<fp32, [1, 32, 80, 12040]> _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")];
+            tensor<fp32, [1, 32, 80, 12040]> add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")];
+            tensor<fp32, [1, 32, 80, 12040]> relu_6 = relu(x = add_2)[name = string("relu_6")];
+            string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_215 = const()[name = string("const_215"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))];
+            tensor<fp32, [64]> const_216 = const()[name = string("const_216"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")];
+            string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_217 = const()[name = string("const_217"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))];
+            tensor<fp32, [64]> const_218 = const()[name = string("const_218"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")];
+            string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_219 = const()[name = string("const_219"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))];
+            tensor<fp32, [64]> const_220 = const()[name = string("const_220"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")];
+            tensor<fp32, [1, 64, 40, 6020]> add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_8 = relu(x = add_3)[name = string("relu_8")];
+            string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_221 = const()[name = string("const_221"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))];
+            tensor<fp32, [64]> const_222 = const()[name = string("const_222"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")];
+            string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_223 = const()[name = string("const_223"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))];
+            tensor<fp32, [64]> const_224 = const()[name = string("const_224"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")];
+            tensor<fp32, [1, 64, 40, 6020]> add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_10 = relu(x = add_4)[name = string("relu_10")];
+            string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_225 = const()[name = string("const_225"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))];
+            tensor<fp32, [64]> const_226 = const()[name = string("const_226"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")];
+            string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_227 = const()[name = string("const_227"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))];
+            tensor<fp32, [64]> const_228 = const()[name = string("const_228"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")];
+            tensor<fp32, [1, 64, 40, 6020]> add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_12 = relu(x = add_5)[name = string("relu_12")];
+            string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_229 = const()[name = string("const_229"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))];
+            tensor<fp32, [64]> const_230 = const()[name = string("const_230"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")];
+            string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_231 = const()[name = string("const_231"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))];
+            tensor<fp32, [64]> const_232 = const()[name = string("const_232"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))];
+            tensor<fp32, [1, 64, 40, 6020]> _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")];
+            tensor<fp32, [1, 64, 40, 6020]> add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")];
+            tensor<fp32, [1, 64, 40, 6020]> relu_14 = relu(x = add_6)[name = string("relu_14")];
+            string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_233 = const()[name = string("const_233"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))];
+            tensor<fp32, [128]> const_234 = const()[name = string("const_234"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")];
+            string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_235 = const()[name = string("const_235"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))];
+            tensor<fp32, [128]> const_236 = const()[name = string("const_236"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")];
+            string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_237 = const()[name = string("const_237"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))];
+            tensor<fp32, [128]> const_238 = const()[name = string("const_238"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")];
+            tensor<fp32, [1, 128, 20, 3010]> add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_16 = relu(x = add_7)[name = string("relu_16")];
+            string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_239 = const()[name = string("const_239"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))];
+            tensor<fp32, [128]> const_240 = const()[name = string("const_240"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")];
+            string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_241 = const()[name = string("const_241"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))];
+            tensor<fp32, [128]> const_242 = const()[name = string("const_242"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")];
+            tensor<fp32, [1, 128, 20, 3010]> add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_18 = relu(x = add_8)[name = string("relu_18")];
+            string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_243 = const()[name = string("const_243"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))];
+            tensor<fp32, [128]> const_244 = const()[name = string("const_244"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")];
+            string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_245 = const()[name = string("const_245"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))];
+            tensor<fp32, [128]> const_246 = const()[name = string("const_246"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")];
+            tensor<fp32, [1, 128, 20, 3010]> add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_20 = relu(x = add_9)[name = string("relu_20")];
+            string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_247 = const()[name = string("const_247"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))];
+            tensor<fp32, [128]> const_248 = const()[name = string("const_248"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")];
+            string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_249 = const()[name = string("const_249"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))];
+            tensor<fp32, [128]> const_250 = const()[name = string("const_250"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")];
+            tensor<fp32, [1, 128, 20, 3010]> add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_22 = relu(x = add_10)[name = string("relu_22")];
+            string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_251 = const()[name = string("const_251"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))];
+            tensor<fp32, [128]> const_252 = const()[name = string("const_252"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")];
+            string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_253 = const()[name = string("const_253"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))];
+            tensor<fp32, [128]> const_254 = const()[name = string("const_254"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")];
+            tensor<fp32, [1, 128, 20, 3010]> add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_24 = relu(x = add_11)[name = string("relu_24")];
+            string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_255 = const()[name = string("const_255"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))];
+            tensor<fp32, [128]> const_256 = const()[name = string("const_256"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")];
+            string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_257 = const()[name = string("const_257"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))];
+            tensor<fp32, [128]> const_258 = const()[name = string("const_258"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))];
+            tensor<fp32, [1, 128, 20, 3010]> _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")];
+            tensor<fp32, [1, 128, 20, 3010]> add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")];
+            tensor<fp32, [1, 128, 20, 3010]> relu_26 = relu(x = add_12)[name = string("relu_26")];
+            string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_259 = const()[name = string("const_259"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))];
+            tensor<fp32, [256]> const_260 = const()[name = string("const_260"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")];
+            string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_261 = const()[name = string("const_261"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))];
+            tensor<fp32, [256]> const_262 = const()[name = string("const_262"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")];
+            string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_263 = const()[name = string("const_263"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))];
+            tensor<fp32, [256]> const_264 = const()[name = string("const_264"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")];
+            tensor<fp32, [1, 256, 10, 1505]> add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_28 = relu(x = add_13)[name = string("relu_28")];
+            string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_265 = const()[name = string("const_265"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))];
+            tensor<fp32, [256]> const_266 = const()[name = string("const_266"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")];
+            string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_267 = const()[name = string("const_267"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))];
+            tensor<fp32, [256]> const_268 = const()[name = string("const_268"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")];
+            tensor<fp32, [1, 256, 10, 1505]> add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_30 = relu(x = add_14)[name = string("relu_30")];
+            string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_269 = const()[name = string("const_269"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))];
+            tensor<fp32, [256]> const_270 = const()[name = string("const_270"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")];
+            string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_271 = const()[name = string("const_271"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))];
+            tensor<fp32, [256]> const_272 = const()[name = string("const_272"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))];
+            tensor<fp32, [1, 256, 10, 1505]> _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")];
+            tensor<fp32, [1, 256, 10, 1505]> add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")];
+            tensor<fp32, [1, 256, 10, 1505]> relu_32 = relu(x = add_15)[name = string("relu_32")];
+            tensor<int32, [3]> const_179 = const()[name = string("const_179"), val = tensor<int32, [3]>([1, 2560, -1])];
+            tensor<fp32, [1, 2560, 1505]> view = reshape(shape = const_179, x = relu_32)[name = string("view")];
+            tensor<int32, [1]> squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [2560, 1505]> squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")];
+            int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)];
+            bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)];
+            tensor<int32, [14500]> select_0 = const()[name = string("select_0"), val = tensor<int32, [14500]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))];
+            int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)];
+            tensor<fp32, [2560, 14500]> index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")];
+            tensor<int32, [3]> const_182 = const()[name = string("const_182"), val = tensor<int32, [3]>([2560, 116, 125])];
+            tensor<fp32, [2560, 116, 125]> view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")];
+            tensor<int32, [3]> const_183 = const()[name = string("const_183"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, [3]> tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor<int32, [3]>([3, 1, 1])];
+            tensor<fp32, [116, 2560, 125]> permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")];
+            tensor<fp32, [348, 2560, 125]> tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")];
+            tensor<int32, [4]> concat_0 = const()[name = string("concat_0"), val = tensor<int32, [4]>([3, 116, 2560, 125])];
+            tensor<fp32, [3, 116, 2560, 125]> reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")];
+            tensor<int32, [4]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [4]>([1, 0, 2, 3])];
+            tensor<int32, [3]> concat_1 = const()[name = string("concat_1"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [116, 3, 2560, 125]> transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")];
+            tensor<fp32, [348, 2560, 125]> repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")];
+            tensor<int32, [1]> unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [348, 1, 589]> unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [348, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[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, [348, 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]> upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [348, 1, 125]> upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")];
+            tensor<int32, [1]> sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 1]> sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")];
+            fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)];
+            tensor<bool, [348, 1]> gt = greater(x = sum_1, y = const_189)[name = string("gt")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [348, 1]> fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [348, 1]> where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")];
+            tensor<fp32, [348, 2560, 125]> mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")];
+            tensor<int32, [1]> sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 2560]> sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")];
+            tensor<fp32, [348, 2560]> div = real_div(x = sum_2, y = where)[name = string("div")];
+            tensor<int32, [1]> unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [348, 2560, 1]> unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")];
+            tensor<fp32, [348, 2560, 125]> sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")];
+            tensor<fp32, [348, 2560, 125]> square = mul(x = sub, y = sub)[name = string("square")];
+            tensor<fp32, [348, 1, 125]> square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")];
+            tensor<int32, [1]> sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 1]> sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")];
+            tensor<fp32, [348, 1]> div_1 = real_div(x = sum_3, y = where)[name = string("div_1")];
+            tensor<fp32, [348, 1]> sub_1 = sub(x = where, y = div_1)[name = string("sub_1")];
+            fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [348, 1]> add_16 = add(x = sub_1, y = const_193)[name = string("add_16")];
+            tensor<fp32, [348, 2560, 125]> mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")];
+            tensor<int32, [1]> sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [348, 2560]> sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")];
+            tensor<fp32, [348, 2560]> div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")];
+            fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [348, 2560]> clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")];
+            tensor<fp32, [348, 2560]> sqrt = sqrt(x = clamp_min)[name = string("sqrt")];
+            int32 const_196 = const()[name = string("const_196"), val = int32(-1)];
+            bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)];
+            tensor<fp32, [348, 5120]> cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")];
+            tensor<fp32, [348, 2560]> zeros_like = const()[name = string("zeros_like"), val = tensor<fp32, [348, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26583232)))];
+            fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [348, 2560]> full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")];
+            int32 const_198 = const()[name = string("const_198"), val = int32(-1)];
+            bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)];
+            tensor<fp32, [348, 5120]> cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")];
+            fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)];
+            tensor<bool, [348, 1]> le = less_equal(x = sum_1, y = const_199)[name = string("le")];
+            tensor<int32, [2]> const_200 = const()[name = string("const_200"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [348, 5120]> repeat = tile(reps = const_200, x = le)[name = string("repeat")];
+            tensor<fp32, [348, 5120]> where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")];
+            tensor<fp32, [348, 256]> output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-chunk-emb-s12-w116.mlmodelc/weights/weight.bin

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wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/analytics/coremldata.bin

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+size 243

wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
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+size 401

wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/model.mil

@@ -0,0 +1,427 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 3016, 80]> fbank, tensor<fp32, [66, 589]> masks) {
+            tensor<fp32, [256, 5120]> p_resnet_seg_1_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor<fp32, [256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1310848))))[name = string("p_resnet_seg_1_weight_quantized")];
+            tensor<fp32, [256]> p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1311936)))];
+            tensor<int32, [3]> const_0 = const()[name = string("const_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 80, 3016]> permute = transpose(perm = const_0, x = fbank)[name = string("transpose_2")];
+            tensor<fp32, [1, 1, 80, 3016]> unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")];
+            string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_201 = const()[name = string("const_201"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1313024)))];
+            tensor<fp32, [32]> const_202 = const()[name = string("const_202"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314240)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")];
+            tensor<fp32, [1, 32, 80, 3016]> relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")];
+            string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_203_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1314432))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323712))))[name = string("const_203_quantized")];
+            tensor<fp32, [32]> const_204 = const()[name = string("const_204"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1323904)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203_quantized, x = relu)[name = string("_native_batch_norm_legit_no_training_1")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")];
+            string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_205_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1324096))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333376))))[name = string("const_205_quantized")];
+            tensor<fp32, [32]> const_206 = const()[name = string("const_206"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333568)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205_quantized, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")];
+            tensor<fp32, [1, 32, 80, 3016]> add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_2 = relu(x = add)[name = string("relu_2")];
+            string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_207_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1333760))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343040))))[name = string("const_207_quantized")];
+            tensor<fp32, [32]> const_208 = const()[name = string("const_208"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343232)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207_quantized, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")];
+            string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_209_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1343424))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352704))))[name = string("const_209_quantized")];
+            tensor<fp32, [32]> const_210 = const()[name = string("const_210"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1352896)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209_quantized, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")];
+            tensor<fp32, [1, 32, 80, 3016]> add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_4 = relu(x = add_1)[name = string("relu_4")];
+            string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_211_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353088))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362368))))[name = string("const_211_quantized")];
+            tensor<fp32, [32]> const_212 = const()[name = string("const_212"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362560)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211_quantized, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")];
+            string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_213_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1362752))), scale = tensor<fp32, [32, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372032))))[name = string("const_213_quantized")];
+            tensor<fp32, [32]> const_214 = const()[name = string("const_214"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372224)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213_quantized, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")];
+            tensor<fp32, [1, 32, 80, 3016]> add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_6 = relu(x = add_2)[name = string("relu_6")];
+            string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_215_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1372416))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1390912))))[name = string("const_215_quantized")];
+            tensor<fp32, [64]> const_216 = const()[name = string("const_216"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391232)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215_quantized, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")];
+            string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_217_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1391552))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428480))))[name = string("const_217_quantized")];
+            tensor<fp32, [64]> const_218 = const()[name = string("const_218"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1428800)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217_quantized, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")];
+            string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_219 = const()[name = string("const_219"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1429120)))];
+            tensor<fp32, [64]> const_220 = const()[name = string("const_220"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437376)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")];
+            tensor<fp32, [1, 64, 40, 1508]> add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_8 = relu(x = add_3)[name = string("relu_8")];
+            string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_221_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1437696))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474624))))[name = string("const_221_quantized")];
+            tensor<fp32, [64]> const_222 = const()[name = string("const_222"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1474944)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221_quantized, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")];
+            string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_223_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1475264))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512192))))[name = string("const_223_quantized")];
+            tensor<fp32, [64]> const_224 = const()[name = string("const_224"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512512)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223_quantized, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")];
+            tensor<fp32, [1, 64, 40, 1508]> add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_10 = relu(x = add_4)[name = string("relu_10")];
+            string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_225_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1512832))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1549760))))[name = string("const_225_quantized")];
+            tensor<fp32, [64]> const_226 = const()[name = string("const_226"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550080)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225_quantized, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")];
+            string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_227_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1550400))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587328))))[name = string("const_227_quantized")];
+            tensor<fp32, [64]> const_228 = const()[name = string("const_228"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587648)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227_quantized, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")];
+            tensor<fp32, [1, 64, 40, 1508]> add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_12 = relu(x = add_5)[name = string("relu_12")];
+            string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_229_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1587968))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1624896))))[name = string("const_229_quantized")];
+            tensor<fp32, [64]> const_230 = const()[name = string("const_230"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625216)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229_quantized, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")];
+            string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_231_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1625536))), scale = tensor<fp32, [64, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662464))))[name = string("const_231_quantized")];
+            tensor<fp32, [64]> const_232 = const()[name = string("const_232"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1662784)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231_quantized, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")];
+            tensor<fp32, [1, 64, 40, 1508]> add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_14 = relu(x = add_6)[name = string("relu_14")];
+            string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_233_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1663104))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1736896))))[name = string("const_233_quantized")];
+            tensor<fp32, [128]> const_234 = const()[name = string("const_234"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1737472)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")];
+            tensor<fp32, [1, 128, 20, 754]> relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")];
+            string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_235_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1738048))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1885568))))[name = string("const_235_quantized")];
+            tensor<fp32, [128]> const_236 = const()[name = string("const_236"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886144)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235_quantized, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")];
+            string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_237_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1886720))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1894976))))[name = string("const_237_quantized")];
+            tensor<fp32, [128]> const_238 = const()[name = string("const_238"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1895552)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237_quantized, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")];
+            tensor<fp32, [1, 128, 20, 754]> add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")];
+            tensor<fp32, [1, 128, 20, 754]> relu_16 = relu(x = add_7)[name = string("relu_16")];
+            string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_239_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1896128))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2043648))))[name = string("const_239_quantized")];
+            tensor<fp32, [128]> const_240 = const()[name = string("const_240"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044224)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239_quantized, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")];
+            tensor<fp32, [1, 128, 20, 754]> relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")];
+            string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_241_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2044800))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192320))))[name = string("const_241_quantized")];
+            tensor<fp32, [128]> const_242 = const()[name = string("const_242"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2192896)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241_quantized, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")];
+            tensor<fp32, [1, 128, 20, 754]> add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")];
+            tensor<fp32, [1, 128, 20, 754]> relu_18 = relu(x = add_8)[name = string("relu_18")];
+            string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_243_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2193472))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2340992))))[name = string("const_243_quantized")];
+            tensor<fp32, [128]> const_244 = const()[name = string("const_244"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2341568)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243_quantized, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")];
+            tensor<fp32, [1, 128, 20, 754]> relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")];
+            string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_245_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2342144))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2489664))))[name = string("const_245_quantized")];
+            tensor<fp32, [128]> const_246 = const()[name = string("const_246"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490240)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245_quantized, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")];
+            tensor<fp32, [1, 128, 20, 754]> add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")];
+            tensor<fp32, [1, 128, 20, 754]> relu_20 = relu(x = add_9)[name = string("relu_20")];
+            string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_247_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2490816))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638336))))[name = string("const_247_quantized")];
+            tensor<fp32, [128]> const_248 = const()[name = string("const_248"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2638912)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247_quantized, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")];
+            tensor<fp32, [1, 128, 20, 754]> relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")];
+            string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_249_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2639488))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787008))))[name = string("const_249_quantized")];
+            tensor<fp32, [128]> const_250 = const()[name = string("const_250"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2787584)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249_quantized, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")];
+            tensor<fp32, [1, 128, 20, 754]> add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")];
+            tensor<fp32, [1, 128, 20, 754]> relu_22 = relu(x = add_10)[name = string("relu_22")];
+            string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_251_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2788160))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2935680))))[name = string("const_251_quantized")];
+            tensor<fp32, [128]> const_252 = const()[name = string("const_252"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936256)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251_quantized, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")];
+            tensor<fp32, [1, 128, 20, 754]> relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")];
+            string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_253_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2936832))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084352))))[name = string("const_253_quantized")];
+            tensor<fp32, [128]> const_254 = const()[name = string("const_254"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3084928)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253_quantized, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")];
+            tensor<fp32, [1, 128, 20, 754]> add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")];
+            tensor<fp32, [1, 128, 20, 754]> relu_24 = relu(x = add_11)[name = string("relu_24")];
+            string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_255_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3085504))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233024))))[name = string("const_255_quantized")];
+            tensor<fp32, [128]> const_256 = const()[name = string("const_256"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3233600)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255_quantized, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")];
+            tensor<fp32, [1, 128, 20, 754]> relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")];
+            string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_257_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3234176))), scale = tensor<fp32, [128, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3381696))))[name = string("const_257_quantized")];
+            tensor<fp32, [128]> const_258 = const()[name = string("const_258"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382272)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257_quantized, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")];
+            tensor<fp32, [1, 128, 20, 754]> add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")];
+            tensor<fp32, [1, 128, 20, 754]> relu_26 = relu(x = add_12)[name = string("relu_26")];
+            string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_259_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3382848))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3677824))))[name = string("const_259_quantized")];
+            tensor<fp32, [256]> const_260 = const()[name = string("const_260"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3678912)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")];
+            tensor<fp32, [1, 256, 10, 377]> relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")];
+            string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_261_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3680000))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4269888))))[name = string("const_261_quantized")];
+            tensor<fp32, [256]> const_262 = const()[name = string("const_262"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4270976)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261_quantized, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")];
+            string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_263_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4272064))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4304896))))[name = string("const_263_quantized")];
+            tensor<fp32, [256]> const_264 = const()[name = string("const_264"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4305984)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263_quantized, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")];
+            tensor<fp32, [1, 256, 10, 377]> add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")];
+            tensor<fp32, [1, 256, 10, 377]> relu_28 = relu(x = add_13)[name = string("relu_28")];
+            string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_265_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4307072))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4896960))))[name = string("const_265_quantized")];
+            tensor<fp32, [256]> const_266 = const()[name = string("const_266"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4898048)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265_quantized, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")];
+            tensor<fp32, [1, 256, 10, 377]> relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")];
+            string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_267_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4899136))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5489024))))[name = string("const_267_quantized")];
+            tensor<fp32, [256]> const_268 = const()[name = string("const_268"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5490112)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267_quantized, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")];
+            tensor<fp32, [1, 256, 10, 377]> add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")];
+            tensor<fp32, [1, 256, 10, 377]> relu_30 = relu(x = add_14)[name = string("relu_30")];
+            string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_269_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5491200))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6081088))))[name = string("const_269_quantized")];
+            tensor<fp32, [256]> const_270 = const()[name = string("const_270"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6082176)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269_quantized, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")];
+            tensor<fp32, [1, 256, 10, 377]> relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")];
+            string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_271_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6083264))), scale = tensor<fp32, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6673152))))[name = string("const_271_quantized")];
+            tensor<fp32, [256]> const_272 = const()[name = string("const_272"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6674240)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271_quantized, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")];
+            tensor<fp32, [1, 256, 10, 377]> add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")];
+            tensor<fp32, [1, 256, 10, 377]> relu_32 = relu(x = add_15)[name = string("relu_32")];
+            tensor<int32, [3]> const_179 = const()[name = string("const_179"), val = tensor<int32, [3]>([1, 2560, -1])];
+            tensor<fp32, [1, 2560, 377]> view = reshape(shape = const_179, x = relu_32)[name = string("view")];
+            tensor<int32, [1]> squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [2560, 377]> squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")];
+            int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)];
+            bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)];
+            tensor<int32, [2750]> select_0 = const()[name = string("select_0"), val = tensor<int32, [2750]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6675328)))];
+            int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)];
+            tensor<fp32, [2560, 2750]> index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")];
+            tensor<int32, [3]> const_182 = const()[name = string("const_182"), val = tensor<int32, [3]>([2560, 22, 125])];
+            tensor<fp32, [2560, 22, 125]> view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")];
+            tensor<int32, [3]> const_183 = const()[name = string("const_183"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, [3]> tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor<int32, [3]>([3, 1, 1])];
+            tensor<fp32, [22, 2560, 125]> permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_1")];
+            tensor<fp32, [66, 2560, 125]> tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")];
+            tensor<int32, [4]> concat_0 = const()[name = string("concat_0"), val = tensor<int32, [4]>([3, 22, 2560, 125])];
+            tensor<fp32, [3, 22, 2560, 125]> reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")];
+            tensor<int32, [4]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [4]>([1, 0, 2, 3])];
+            tensor<int32, [3]> concat_1 = const()[name = string("concat_1"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [22, 3, 2560, 125]> transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_0")];
+            tensor<fp32, [66, 2560, 125]> repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")];
+            tensor<int32, [1]> unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [66, 1, 589]> unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [66, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[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, [66, 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]> upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [66, 1, 125]> upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")];
+            tensor<int32, [1]> sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 1]> sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")];
+            fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)];
+            tensor<bool, [66, 1]> gt = greater(x = sum_1, y = const_189)[name = string("gt")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [66, 1]> fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [66, 1]> where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")];
+            tensor<fp32, [66, 2560, 125]> mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")];
+            tensor<int32, [1]> sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 2560]> sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")];
+            tensor<fp32, [66, 2560]> div = real_div(x = sum_2, y = where)[name = string("div")];
+            tensor<int32, [1]> unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [66, 2560, 1]> unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")];
+            tensor<fp32, [66, 2560, 125]> sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")];
+            tensor<fp32, [66, 2560, 125]> square = mul(x = sub, y = sub)[name = string("square")];
+            tensor<fp32, [66, 1, 125]> square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")];
+            tensor<int32, [1]> sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 1]> sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")];
+            tensor<fp32, [66, 1]> div_1 = real_div(x = sum_3, y = where)[name = string("div_1")];
+            tensor<fp32, [66, 1]> sub_1 = sub(x = where, y = div_1)[name = string("sub_1")];
+            fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [66, 1]> add_16 = add(x = sub_1, y = const_193)[name = string("add_16")];
+            tensor<fp32, [66, 2560, 125]> mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")];
+            tensor<int32, [1]> sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 2560]> sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")];
+            tensor<fp32, [66, 2560]> div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")];
+            fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [66, 2560]> clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")];
+            tensor<fp32, [66, 2560]> sqrt = sqrt(x = clamp_min)[name = string("sqrt")];
+            int32 const_196 = const()[name = string("const_196"), val = int32(-1)];
+            bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)];
+            tensor<fp32, [66, 5120]> cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")];
+            tensor<fp32, [66, 2560]> zeros_like_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [66, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6686400))), scale = tensor<fp32, [66, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6855424))))[name = string("zeros_like_quantized")];
+            fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [66, 2560]> full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")];
+            int32 const_198 = const()[name = string("const_198"), val = int32(-1)];
+            bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)];
+            tensor<fp32, [66, 5120]> cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like_quantized, full_like))[name = string("cat_1")];
+            fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)];
+            tensor<bool, [66, 1]> le = less_equal(x = sum_1, y = const_199)[name = string("le")];
+            tensor<int32, [2]> const_200 = const()[name = string("const_200"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [66, 5120]> repeat = tile(reps = const_200, x = le)[name = string("repeat")];
+            tensor<fp32, [66, 5120]> where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")];
+            tensor<fp32, [66, 256]> output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight_quantized, x = where_1)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-chunk-emb-s12-w22-w8a16.mlmodelc/weights/weight.bin

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+size 6855752

wespeaker-chunk-emb-s12-w22.mlmodelc/analytics/coremldata.bin

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+size 243

wespeaker-chunk-emb-s12-w22.mlmodelc/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
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+size 170

wespeaker-chunk-emb-s12-w22.mlmodelc/model.mil

@@ -0,0 +1,427 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [1, 3016, 80]> fbank, tensor<fp32, [66, 589]> masks) {
+            tensor<fp32, [256, 5120]> p_resnet_seg_1_weight = const()[name = string("p_resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [256]> p_resnet_seg_1_bias = const()[name = string("p_resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5243008)))];
+            tensor<int32, [3]> const_0 = const()[name = string("const_0"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> unsqueeze_axes_0 = const()[name = string("unsqueeze_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [1, 80, 3016]> permute = transpose(perm = const_0, x = fbank)[name = string("transpose_3")];
+            tensor<fp32, [1, 1, 80, 3016]> unsqueeze = expand_dims(axes = unsqueeze_axes_0, x = permute)[name = string("unsqueeze")];
+            string conv2d_pad_type_0 = const()[name = string("conv2d_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_pad_0 = const()[name = string("conv2d_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_strides_0 = const()[name = string("conv2d_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_dilations_0 = const()[name = string("conv2d_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_groups_0 = const()[name = string("conv2d_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_201 = const()[name = string("const_201"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5244096)))];
+            tensor<fp32, [32]> const_202 = const()[name = string("const_202"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245312)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training = conv(bias = const_202, dilations = conv2d_dilations_0, groups = conv2d_groups_0, pad = conv2d_pad_0, pad_type = conv2d_pad_type_0, strides = conv2d_strides_0, weight = const_201, x = unsqueeze)[name = string("_native_batch_norm_legit_no_training")];
+            tensor<fp32, [1, 32, 80, 3016]> relu = relu(x = _native_batch_norm_legit_no_training)[name = string("relu")];
+            string conv2d_1_pad_type_0 = const()[name = string("conv2d_1_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_1_pad_0 = const()[name = string("conv2d_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_1_strides_0 = const()[name = string("conv2d_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_1_dilations_0 = const()[name = string("conv2d_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_1_groups_0 = const()[name = string("conv2d_1_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_203 = const()[name = string("const_203"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5245504)))];
+            tensor<fp32, [32]> const_204 = const()[name = string("const_204"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282432)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_1 = conv(bias = const_204, dilations = conv2d_1_dilations_0, groups = conv2d_1_groups_0, pad = conv2d_1_pad_0, pad_type = conv2d_1_pad_type_0, strides = conv2d_1_strides_0, weight = const_203, x = relu)[name = string("_native_batch_norm_legit_no_training_1")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_1 = relu(x = _native_batch_norm_legit_no_training_1)[name = string("relu_1")];
+            string conv2d_2_pad_type_0 = const()[name = string("conv2d_2_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_2_pad_0 = const()[name = string("conv2d_2_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_2_strides_0 = const()[name = string("conv2d_2_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_2_dilations_0 = const()[name = string("conv2d_2_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_2_groups_0 = const()[name = string("conv2d_2_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_205 = const()[name = string("const_205"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5282624)))];
+            tensor<fp32, [32]> const_206 = const()[name = string("const_206"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319552)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_2 = conv(bias = const_206, dilations = conv2d_2_dilations_0, groups = conv2d_2_groups_0, pad = conv2d_2_pad_0, pad_type = conv2d_2_pad_type_0, strides = conv2d_2_strides_0, weight = const_205, x = relu_1)[name = string("_native_batch_norm_legit_no_training_2")];
+            tensor<fp32, [1, 32, 80, 3016]> add = add(x = _native_batch_norm_legit_no_training_2, y = relu)[name = string("add")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_2 = relu(x = add)[name = string("relu_2")];
+            string conv2d_3_pad_type_0 = const()[name = string("conv2d_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_3_pad_0 = const()[name = string("conv2d_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_3_strides_0 = const()[name = string("conv2d_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_3_dilations_0 = const()[name = string("conv2d_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_3_groups_0 = const()[name = string("conv2d_3_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_207 = const()[name = string("const_207"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5319744)))];
+            tensor<fp32, [32]> const_208 = const()[name = string("const_208"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356672)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_3 = conv(bias = const_208, dilations = conv2d_3_dilations_0, groups = conv2d_3_groups_0, pad = conv2d_3_pad_0, pad_type = conv2d_3_pad_type_0, strides = conv2d_3_strides_0, weight = const_207, x = relu_2)[name = string("_native_batch_norm_legit_no_training_3")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_3 = relu(x = _native_batch_norm_legit_no_training_3)[name = string("relu_3")];
+            string conv2d_4_pad_type_0 = const()[name = string("conv2d_4_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_4_pad_0 = const()[name = string("conv2d_4_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_4_strides_0 = const()[name = string("conv2d_4_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_4_dilations_0 = const()[name = string("conv2d_4_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_4_groups_0 = const()[name = string("conv2d_4_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_209 = const()[name = string("const_209"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5356864)))];
+            tensor<fp32, [32]> const_210 = const()[name = string("const_210"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393792)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_4 = conv(bias = const_210, dilations = conv2d_4_dilations_0, groups = conv2d_4_groups_0, pad = conv2d_4_pad_0, pad_type = conv2d_4_pad_type_0, strides = conv2d_4_strides_0, weight = const_209, x = relu_3)[name = string("_native_batch_norm_legit_no_training_4")];
+            tensor<fp32, [1, 32, 80, 3016]> add_1 = add(x = _native_batch_norm_legit_no_training_4, y = relu_2)[name = string("add_1")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_4 = relu(x = add_1)[name = string("relu_4")];
+            string conv2d_5_pad_type_0 = const()[name = string("conv2d_5_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_5_pad_0 = const()[name = string("conv2d_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_5_strides_0 = const()[name = string("conv2d_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_5_dilations_0 = const()[name = string("conv2d_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_5_groups_0 = const()[name = string("conv2d_5_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_211 = const()[name = string("const_211"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5393984)))];
+            tensor<fp32, [32]> const_212 = const()[name = string("const_212"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5430912)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_5 = conv(bias = const_212, dilations = conv2d_5_dilations_0, groups = conv2d_5_groups_0, pad = conv2d_5_pad_0, pad_type = conv2d_5_pad_type_0, strides = conv2d_5_strides_0, weight = const_211, x = relu_4)[name = string("_native_batch_norm_legit_no_training_5")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_5 = relu(x = _native_batch_norm_legit_no_training_5)[name = string("relu_5")];
+            string conv2d_6_pad_type_0 = const()[name = string("conv2d_6_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_6_pad_0 = const()[name = string("conv2d_6_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_6_strides_0 = const()[name = string("conv2d_6_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_6_dilations_0 = const()[name = string("conv2d_6_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_6_groups_0 = const()[name = string("conv2d_6_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_213 = const()[name = string("const_213"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5431104)))];
+            tensor<fp32, [32]> const_214 = const()[name = string("const_214"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468032)))];
+            tensor<fp32, [1, 32, 80, 3016]> _native_batch_norm_legit_no_training_6 = conv(bias = const_214, dilations = conv2d_6_dilations_0, groups = conv2d_6_groups_0, pad = conv2d_6_pad_0, pad_type = conv2d_6_pad_type_0, strides = conv2d_6_strides_0, weight = const_213, x = relu_5)[name = string("_native_batch_norm_legit_no_training_6")];
+            tensor<fp32, [1, 32, 80, 3016]> add_2 = add(x = _native_batch_norm_legit_no_training_6, y = relu_4)[name = string("add_2")];
+            tensor<fp32, [1, 32, 80, 3016]> relu_6 = relu(x = add_2)[name = string("relu_6")];
+            string conv2d_7_pad_type_0 = const()[name = string("conv2d_7_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_7_pad_0 = const()[name = string("conv2d_7_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_7_strides_0 = const()[name = string("conv2d_7_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_7_dilations_0 = const()[name = string("conv2d_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_7_groups_0 = const()[name = string("conv2d_7_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_215 = const()[name = string("const_215"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5468224)))];
+            tensor<fp32, [64]> const_216 = const()[name = string("const_216"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542016)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_7 = conv(bias = const_216, dilations = conv2d_7_dilations_0, groups = conv2d_7_groups_0, pad = conv2d_7_pad_0, pad_type = conv2d_7_pad_type_0, strides = conv2d_7_strides_0, weight = const_215, x = relu_6)[name = string("_native_batch_norm_legit_no_training_7")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_7 = relu(x = _native_batch_norm_legit_no_training_7)[name = string("relu_7")];
+            string conv2d_8_pad_type_0 = const()[name = string("conv2d_8_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_8_pad_0 = const()[name = string("conv2d_8_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_8_strides_0 = const()[name = string("conv2d_8_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_8_dilations_0 = const()[name = string("conv2d_8_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_8_groups_0 = const()[name = string("conv2d_8_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_217 = const()[name = string("const_217"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5542336)))];
+            tensor<fp32, [64]> const_218 = const()[name = string("const_218"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5689856)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_8 = conv(bias = const_218, dilations = conv2d_8_dilations_0, groups = conv2d_8_groups_0, pad = conv2d_8_pad_0, pad_type = conv2d_8_pad_type_0, strides = conv2d_8_strides_0, weight = const_217, x = relu_7)[name = string("_native_batch_norm_legit_no_training_8")];
+            string conv2d_9_pad_type_0 = const()[name = string("conv2d_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_9_strides_0 = const()[name = string("conv2d_9_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_9_pad_0 = const()[name = string("conv2d_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_9_dilations_0 = const()[name = string("conv2d_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_9_groups_0 = const()[name = string("conv2d_9_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_219 = const()[name = string("const_219"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5690176)))];
+            tensor<fp32, [64]> const_220 = const()[name = string("const_220"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698432)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_9 = conv(bias = const_220, dilations = conv2d_9_dilations_0, groups = conv2d_9_groups_0, pad = conv2d_9_pad_0, pad_type = conv2d_9_pad_type_0, strides = conv2d_9_strides_0, weight = const_219, x = relu_6)[name = string("_native_batch_norm_legit_no_training_9")];
+            tensor<fp32, [1, 64, 40, 1508]> add_3 = add(x = _native_batch_norm_legit_no_training_8, y = _native_batch_norm_legit_no_training_9)[name = string("add_3")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_8 = relu(x = add_3)[name = string("relu_8")];
+            string conv2d_10_pad_type_0 = const()[name = string("conv2d_10_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_10_pad_0 = const()[name = string("conv2d_10_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_10_strides_0 = const()[name = string("conv2d_10_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_10_dilations_0 = const()[name = string("conv2d_10_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_10_groups_0 = const()[name = string("conv2d_10_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_221 = const()[name = string("const_221"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5698752)))];
+            tensor<fp32, [64]> const_222 = const()[name = string("const_222"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846272)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_10 = conv(bias = const_222, dilations = conv2d_10_dilations_0, groups = conv2d_10_groups_0, pad = conv2d_10_pad_0, pad_type = conv2d_10_pad_type_0, strides = conv2d_10_strides_0, weight = const_221, x = relu_8)[name = string("_native_batch_norm_legit_no_training_10")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_9 = relu(x = _native_batch_norm_legit_no_training_10)[name = string("relu_9")];
+            string conv2d_11_pad_type_0 = const()[name = string("conv2d_11_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_11_pad_0 = const()[name = string("conv2d_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_11_strides_0 = const()[name = string("conv2d_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_11_dilations_0 = const()[name = string("conv2d_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_11_groups_0 = const()[name = string("conv2d_11_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_223 = const()[name = string("const_223"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5846592)))];
+            tensor<fp32, [64]> const_224 = const()[name = string("const_224"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994112)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_11 = conv(bias = const_224, dilations = conv2d_11_dilations_0, groups = conv2d_11_groups_0, pad = conv2d_11_pad_0, pad_type = conv2d_11_pad_type_0, strides = conv2d_11_strides_0, weight = const_223, x = relu_9)[name = string("_native_batch_norm_legit_no_training_11")];
+            tensor<fp32, [1, 64, 40, 1508]> add_4 = add(x = _native_batch_norm_legit_no_training_11, y = relu_8)[name = string("add_4")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_10 = relu(x = add_4)[name = string("relu_10")];
+            string conv2d_12_pad_type_0 = const()[name = string("conv2d_12_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_12_pad_0 = const()[name = string("conv2d_12_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_12_strides_0 = const()[name = string("conv2d_12_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_12_dilations_0 = const()[name = string("conv2d_12_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_12_groups_0 = const()[name = string("conv2d_12_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_225 = const()[name = string("const_225"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5994432)))];
+            tensor<fp32, [64]> const_226 = const()[name = string("const_226"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6141952)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_12 = conv(bias = const_226, dilations = conv2d_12_dilations_0, groups = conv2d_12_groups_0, pad = conv2d_12_pad_0, pad_type = conv2d_12_pad_type_0, strides = conv2d_12_strides_0, weight = const_225, x = relu_10)[name = string("_native_batch_norm_legit_no_training_12")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_11 = relu(x = _native_batch_norm_legit_no_training_12)[name = string("relu_11")];
+            string conv2d_13_pad_type_0 = const()[name = string("conv2d_13_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_13_pad_0 = const()[name = string("conv2d_13_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_13_strides_0 = const()[name = string("conv2d_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_13_dilations_0 = const()[name = string("conv2d_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_13_groups_0 = const()[name = string("conv2d_13_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_227 = const()[name = string("const_227"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6142272)))];
+            tensor<fp32, [64]> const_228 = const()[name = string("const_228"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6289792)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_13 = conv(bias = const_228, dilations = conv2d_13_dilations_0, groups = conv2d_13_groups_0, pad = conv2d_13_pad_0, pad_type = conv2d_13_pad_type_0, strides = conv2d_13_strides_0, weight = const_227, x = relu_11)[name = string("_native_batch_norm_legit_no_training_13")];
+            tensor<fp32, [1, 64, 40, 1508]> add_5 = add(x = _native_batch_norm_legit_no_training_13, y = relu_10)[name = string("add_5")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_12 = relu(x = add_5)[name = string("relu_12")];
+            string conv2d_14_pad_type_0 = const()[name = string("conv2d_14_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_14_pad_0 = const()[name = string("conv2d_14_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_14_strides_0 = const()[name = string("conv2d_14_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_14_dilations_0 = const()[name = string("conv2d_14_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_14_groups_0 = const()[name = string("conv2d_14_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_229 = const()[name = string("const_229"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6290112)))];
+            tensor<fp32, [64]> const_230 = const()[name = string("const_230"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437632)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_14 = conv(bias = const_230, dilations = conv2d_14_dilations_0, groups = conv2d_14_groups_0, pad = conv2d_14_pad_0, pad_type = conv2d_14_pad_type_0, strides = conv2d_14_strides_0, weight = const_229, x = relu_12)[name = string("_native_batch_norm_legit_no_training_14")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_13 = relu(x = _native_batch_norm_legit_no_training_14)[name = string("relu_13")];
+            string conv2d_15_pad_type_0 = const()[name = string("conv2d_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_15_pad_0 = const()[name = string("conv2d_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_15_strides_0 = const()[name = string("conv2d_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_15_dilations_0 = const()[name = string("conv2d_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_15_groups_0 = const()[name = string("conv2d_15_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_231 = const()[name = string("const_231"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6437952)))];
+            tensor<fp32, [64]> const_232 = const()[name = string("const_232"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585472)))];
+            tensor<fp32, [1, 64, 40, 1508]> _native_batch_norm_legit_no_training_15 = conv(bias = const_232, dilations = conv2d_15_dilations_0, groups = conv2d_15_groups_0, pad = conv2d_15_pad_0, pad_type = conv2d_15_pad_type_0, strides = conv2d_15_strides_0, weight = const_231, x = relu_13)[name = string("_native_batch_norm_legit_no_training_15")];
+            tensor<fp32, [1, 64, 40, 1508]> add_6 = add(x = _native_batch_norm_legit_no_training_15, y = relu_12)[name = string("add_6")];
+            tensor<fp32, [1, 64, 40, 1508]> relu_14 = relu(x = add_6)[name = string("relu_14")];
+            string conv2d_16_pad_type_0 = const()[name = string("conv2d_16_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_16_pad_0 = const()[name = string("conv2d_16_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_16_strides_0 = const()[name = string("conv2d_16_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_16_dilations_0 = const()[name = string("conv2d_16_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_16_groups_0 = const()[name = string("conv2d_16_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_233 = const()[name = string("const_233"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6585792)))];
+            tensor<fp32, [128]> const_234 = const()[name = string("const_234"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6880768)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_16 = conv(bias = const_234, dilations = conv2d_16_dilations_0, groups = conv2d_16_groups_0, pad = conv2d_16_pad_0, pad_type = conv2d_16_pad_type_0, strides = conv2d_16_strides_0, weight = const_233, x = relu_14)[name = string("_native_batch_norm_legit_no_training_16")];
+            tensor<fp32, [1, 128, 20, 754]> relu_15 = relu(x = _native_batch_norm_legit_no_training_16)[name = string("relu_15")];
+            string conv2d_17_pad_type_0 = const()[name = string("conv2d_17_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_17_pad_0 = const()[name = string("conv2d_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_17_strides_0 = const()[name = string("conv2d_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_17_dilations_0 = const()[name = string("conv2d_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_17_groups_0 = const()[name = string("conv2d_17_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_235 = const()[name = string("const_235"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6881344)))];
+            tensor<fp32, [128]> const_236 = const()[name = string("const_236"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471232)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_17 = conv(bias = const_236, dilations = conv2d_17_dilations_0, groups = conv2d_17_groups_0, pad = conv2d_17_pad_0, pad_type = conv2d_17_pad_type_0, strides = conv2d_17_strides_0, weight = const_235, x = relu_15)[name = string("_native_batch_norm_legit_no_training_17")];
+            string conv2d_18_pad_type_0 = const()[name = string("conv2d_18_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_18_strides_0 = const()[name = string("conv2d_18_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_18_pad_0 = const()[name = string("conv2d_18_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_18_dilations_0 = const()[name = string("conv2d_18_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_18_groups_0 = const()[name = string("conv2d_18_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_237 = const()[name = string("const_237"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7471808)))];
+            tensor<fp32, [128]> const_238 = const()[name = string("const_238"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7504640)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_18 = conv(bias = const_238, dilations = conv2d_18_dilations_0, groups = conv2d_18_groups_0, pad = conv2d_18_pad_0, pad_type = conv2d_18_pad_type_0, strides = conv2d_18_strides_0, weight = const_237, x = relu_14)[name = string("_native_batch_norm_legit_no_training_18")];
+            tensor<fp32, [1, 128, 20, 754]> add_7 = add(x = _native_batch_norm_legit_no_training_17, y = _native_batch_norm_legit_no_training_18)[name = string("add_7")];
+            tensor<fp32, [1, 128, 20, 754]> relu_16 = relu(x = add_7)[name = string("relu_16")];
+            string conv2d_19_pad_type_0 = const()[name = string("conv2d_19_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_19_pad_0 = const()[name = string("conv2d_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_19_strides_0 = const()[name = string("conv2d_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_19_dilations_0 = const()[name = string("conv2d_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_19_groups_0 = const()[name = string("conv2d_19_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_239 = const()[name = string("const_239"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7505216)))];
+            tensor<fp32, [128]> const_240 = const()[name = string("const_240"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095104)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_19 = conv(bias = const_240, dilations = conv2d_19_dilations_0, groups = conv2d_19_groups_0, pad = conv2d_19_pad_0, pad_type = conv2d_19_pad_type_0, strides = conv2d_19_strides_0, weight = const_239, x = relu_16)[name = string("_native_batch_norm_legit_no_training_19")];
+            tensor<fp32, [1, 128, 20, 754]> relu_17 = relu(x = _native_batch_norm_legit_no_training_19)[name = string("relu_17")];
+            string conv2d_20_pad_type_0 = const()[name = string("conv2d_20_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_20_pad_0 = const()[name = string("conv2d_20_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_20_strides_0 = const()[name = string("conv2d_20_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_20_dilations_0 = const()[name = string("conv2d_20_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_20_groups_0 = const()[name = string("conv2d_20_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_241 = const()[name = string("const_241"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8095680)))];
+            tensor<fp32, [128]> const_242 = const()[name = string("const_242"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8685568)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_20 = conv(bias = const_242, dilations = conv2d_20_dilations_0, groups = conv2d_20_groups_0, pad = conv2d_20_pad_0, pad_type = conv2d_20_pad_type_0, strides = conv2d_20_strides_0, weight = const_241, x = relu_17)[name = string("_native_batch_norm_legit_no_training_20")];
+            tensor<fp32, [1, 128, 20, 754]> add_8 = add(x = _native_batch_norm_legit_no_training_20, y = relu_16)[name = string("add_8")];
+            tensor<fp32, [1, 128, 20, 754]> relu_18 = relu(x = add_8)[name = string("relu_18")];
+            string conv2d_21_pad_type_0 = const()[name = string("conv2d_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_21_pad_0 = const()[name = string("conv2d_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_21_strides_0 = const()[name = string("conv2d_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_21_dilations_0 = const()[name = string("conv2d_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_21_groups_0 = const()[name = string("conv2d_21_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_243 = const()[name = string("const_243"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8686144)))];
+            tensor<fp32, [128]> const_244 = const()[name = string("const_244"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276032)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_21 = conv(bias = const_244, dilations = conv2d_21_dilations_0, groups = conv2d_21_groups_0, pad = conv2d_21_pad_0, pad_type = conv2d_21_pad_type_0, strides = conv2d_21_strides_0, weight = const_243, x = relu_18)[name = string("_native_batch_norm_legit_no_training_21")];
+            tensor<fp32, [1, 128, 20, 754]> relu_19 = relu(x = _native_batch_norm_legit_no_training_21)[name = string("relu_19")];
+            string conv2d_22_pad_type_0 = const()[name = string("conv2d_22_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_22_pad_0 = const()[name = string("conv2d_22_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_22_strides_0 = const()[name = string("conv2d_22_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_22_dilations_0 = const()[name = string("conv2d_22_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_22_groups_0 = const()[name = string("conv2d_22_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_245 = const()[name = string("const_245"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9276608)))];
+            tensor<fp32, [128]> const_246 = const()[name = string("const_246"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9866496)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_22 = conv(bias = const_246, dilations = conv2d_22_dilations_0, groups = conv2d_22_groups_0, pad = conv2d_22_pad_0, pad_type = conv2d_22_pad_type_0, strides = conv2d_22_strides_0, weight = const_245, x = relu_19)[name = string("_native_batch_norm_legit_no_training_22")];
+            tensor<fp32, [1, 128, 20, 754]> add_9 = add(x = _native_batch_norm_legit_no_training_22, y = relu_18)[name = string("add_9")];
+            tensor<fp32, [1, 128, 20, 754]> relu_20 = relu(x = add_9)[name = string("relu_20")];
+            string conv2d_23_pad_type_0 = const()[name = string("conv2d_23_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_23_pad_0 = const()[name = string("conv2d_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_23_strides_0 = const()[name = string("conv2d_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_23_dilations_0 = const()[name = string("conv2d_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_23_groups_0 = const()[name = string("conv2d_23_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_247 = const()[name = string("const_247"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9867072)))];
+            tensor<fp32, [128]> const_248 = const()[name = string("const_248"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10456960)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_23 = conv(bias = const_248, dilations = conv2d_23_dilations_0, groups = conv2d_23_groups_0, pad = conv2d_23_pad_0, pad_type = conv2d_23_pad_type_0, strides = conv2d_23_strides_0, weight = const_247, x = relu_20)[name = string("_native_batch_norm_legit_no_training_23")];
+            tensor<fp32, [1, 128, 20, 754]> relu_21 = relu(x = _native_batch_norm_legit_no_training_23)[name = string("relu_21")];
+            string conv2d_24_pad_type_0 = const()[name = string("conv2d_24_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_24_pad_0 = const()[name = string("conv2d_24_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_24_strides_0 = const()[name = string("conv2d_24_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_24_dilations_0 = const()[name = string("conv2d_24_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_24_groups_0 = const()[name = string("conv2d_24_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_249 = const()[name = string("const_249"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(10457536)))];
+            tensor<fp32, [128]> const_250 = const()[name = string("const_250"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11047424)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_24 = conv(bias = const_250, dilations = conv2d_24_dilations_0, groups = conv2d_24_groups_0, pad = conv2d_24_pad_0, pad_type = conv2d_24_pad_type_0, strides = conv2d_24_strides_0, weight = const_249, x = relu_21)[name = string("_native_batch_norm_legit_no_training_24")];
+            tensor<fp32, [1, 128, 20, 754]> add_10 = add(x = _native_batch_norm_legit_no_training_24, y = relu_20)[name = string("add_10")];
+            tensor<fp32, [1, 128, 20, 754]> relu_22 = relu(x = add_10)[name = string("relu_22")];
+            string conv2d_25_pad_type_0 = const()[name = string("conv2d_25_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_25_pad_0 = const()[name = string("conv2d_25_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_25_strides_0 = const()[name = string("conv2d_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_25_dilations_0 = const()[name = string("conv2d_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_25_groups_0 = const()[name = string("conv2d_25_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_251 = const()[name = string("const_251"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11048000)))];
+            tensor<fp32, [128]> const_252 = const()[name = string("const_252"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11637888)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_25 = conv(bias = const_252, dilations = conv2d_25_dilations_0, groups = conv2d_25_groups_0, pad = conv2d_25_pad_0, pad_type = conv2d_25_pad_type_0, strides = conv2d_25_strides_0, weight = const_251, x = relu_22)[name = string("_native_batch_norm_legit_no_training_25")];
+            tensor<fp32, [1, 128, 20, 754]> relu_23 = relu(x = _native_batch_norm_legit_no_training_25)[name = string("relu_23")];
+            string conv2d_26_pad_type_0 = const()[name = string("conv2d_26_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_26_pad_0 = const()[name = string("conv2d_26_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_26_strides_0 = const()[name = string("conv2d_26_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_26_dilations_0 = const()[name = string("conv2d_26_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_26_groups_0 = const()[name = string("conv2d_26_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_253 = const()[name = string("const_253"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11638464)))];
+            tensor<fp32, [128]> const_254 = const()[name = string("const_254"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228352)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_26 = conv(bias = const_254, dilations = conv2d_26_dilations_0, groups = conv2d_26_groups_0, pad = conv2d_26_pad_0, pad_type = conv2d_26_pad_type_0, strides = conv2d_26_strides_0, weight = const_253, x = relu_23)[name = string("_native_batch_norm_legit_no_training_26")];
+            tensor<fp32, [1, 128, 20, 754]> add_11 = add(x = _native_batch_norm_legit_no_training_26, y = relu_22)[name = string("add_11")];
+            tensor<fp32, [1, 128, 20, 754]> relu_24 = relu(x = add_11)[name = string("relu_24")];
+            string conv2d_27_pad_type_0 = const()[name = string("conv2d_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_27_pad_0 = const()[name = string("conv2d_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_27_strides_0 = const()[name = string("conv2d_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_27_dilations_0 = const()[name = string("conv2d_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_27_groups_0 = const()[name = string("conv2d_27_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_255 = const()[name = string("const_255"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12228928)))];
+            tensor<fp32, [128]> const_256 = const()[name = string("const_256"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12818816)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_27 = conv(bias = const_256, dilations = conv2d_27_dilations_0, groups = conv2d_27_groups_0, pad = conv2d_27_pad_0, pad_type = conv2d_27_pad_type_0, strides = conv2d_27_strides_0, weight = const_255, x = relu_24)[name = string("_native_batch_norm_legit_no_training_27")];
+            tensor<fp32, [1, 128, 20, 754]> relu_25 = relu(x = _native_batch_norm_legit_no_training_27)[name = string("relu_25")];
+            string conv2d_28_pad_type_0 = const()[name = string("conv2d_28_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_28_pad_0 = const()[name = string("conv2d_28_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_28_strides_0 = const()[name = string("conv2d_28_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_28_dilations_0 = const()[name = string("conv2d_28_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_28_groups_0 = const()[name = string("conv2d_28_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_257 = const()[name = string("const_257"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(12819392)))];
+            tensor<fp32, [128]> const_258 = const()[name = string("const_258"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409280)))];
+            tensor<fp32, [1, 128, 20, 754]> _native_batch_norm_legit_no_training_28 = conv(bias = const_258, dilations = conv2d_28_dilations_0, groups = conv2d_28_groups_0, pad = conv2d_28_pad_0, pad_type = conv2d_28_pad_type_0, strides = conv2d_28_strides_0, weight = const_257, x = relu_25)[name = string("_native_batch_norm_legit_no_training_28")];
+            tensor<fp32, [1, 128, 20, 754]> add_12 = add(x = _native_batch_norm_legit_no_training_28, y = relu_24)[name = string("add_12")];
+            tensor<fp32, [1, 128, 20, 754]> relu_26 = relu(x = add_12)[name = string("relu_26")];
+            string conv2d_29_pad_type_0 = const()[name = string("conv2d_29_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_29_pad_0 = const()[name = string("conv2d_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_29_strides_0 = const()[name = string("conv2d_29_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> conv2d_29_dilations_0 = const()[name = string("conv2d_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_29_groups_0 = const()[name = string("conv2d_29_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_259 = const()[name = string("const_259"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13409856)))];
+            tensor<fp32, [256]> const_260 = const()[name = string("const_260"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14589568)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_29 = conv(bias = const_260, dilations = conv2d_29_dilations_0, groups = conv2d_29_groups_0, pad = conv2d_29_pad_0, pad_type = conv2d_29_pad_type_0, strides = conv2d_29_strides_0, weight = const_259, x = relu_26)[name = string("_native_batch_norm_legit_no_training_29")];
+            tensor<fp32, [1, 256, 10, 377]> relu_27 = relu(x = _native_batch_norm_legit_no_training_29)[name = string("relu_27")];
+            string conv2d_30_pad_type_0 = const()[name = string("conv2d_30_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_30_pad_0 = const()[name = string("conv2d_30_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_30_strides_0 = const()[name = string("conv2d_30_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_30_dilations_0 = const()[name = string("conv2d_30_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_30_groups_0 = const()[name = string("conv2d_30_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_261 = const()[name = string("const_261"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14590656)))];
+            tensor<fp32, [256]> const_262 = const()[name = string("const_262"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16950016)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_30 = conv(bias = const_262, dilations = conv2d_30_dilations_0, groups = conv2d_30_groups_0, pad = conv2d_30_pad_0, pad_type = conv2d_30_pad_type_0, strides = conv2d_30_strides_0, weight = const_261, x = relu_27)[name = string("_native_batch_norm_legit_no_training_30")];
+            string conv2d_31_pad_type_0 = const()[name = string("conv2d_31_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> conv2d_31_strides_0 = const()[name = string("conv2d_31_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> conv2d_31_pad_0 = const()[name = string("conv2d_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> conv2d_31_dilations_0 = const()[name = string("conv2d_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_31_groups_0 = const()[name = string("conv2d_31_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_263 = const()[name = string("const_263"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16951104)))];
+            tensor<fp32, [256]> const_264 = const()[name = string("const_264"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17082240)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_31 = conv(bias = const_264, dilations = conv2d_31_dilations_0, groups = conv2d_31_groups_0, pad = conv2d_31_pad_0, pad_type = conv2d_31_pad_type_0, strides = conv2d_31_strides_0, weight = const_263, x = relu_26)[name = string("_native_batch_norm_legit_no_training_31")];
+            tensor<fp32, [1, 256, 10, 377]> add_13 = add(x = _native_batch_norm_legit_no_training_30, y = _native_batch_norm_legit_no_training_31)[name = string("add_13")];
+            tensor<fp32, [1, 256, 10, 377]> relu_28 = relu(x = add_13)[name = string("relu_28")];
+            string conv2d_32_pad_type_0 = const()[name = string("conv2d_32_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_32_pad_0 = const()[name = string("conv2d_32_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_32_strides_0 = const()[name = string("conv2d_32_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_32_dilations_0 = const()[name = string("conv2d_32_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_32_groups_0 = const()[name = string("conv2d_32_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_265 = const()[name = string("const_265"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(17083328)))];
+            tensor<fp32, [256]> const_266 = const()[name = string("const_266"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19442688)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_32 = conv(bias = const_266, dilations = conv2d_32_dilations_0, groups = conv2d_32_groups_0, pad = conv2d_32_pad_0, pad_type = conv2d_32_pad_type_0, strides = conv2d_32_strides_0, weight = const_265, x = relu_28)[name = string("_native_batch_norm_legit_no_training_32")];
+            tensor<fp32, [1, 256, 10, 377]> relu_29 = relu(x = _native_batch_norm_legit_no_training_32)[name = string("relu_29")];
+            string conv2d_33_pad_type_0 = const()[name = string("conv2d_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_33_pad_0 = const()[name = string("conv2d_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_33_strides_0 = const()[name = string("conv2d_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_33_dilations_0 = const()[name = string("conv2d_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_33_groups_0 = const()[name = string("conv2d_33_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_267 = const()[name = string("const_267"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(19443776)))];
+            tensor<fp32, [256]> const_268 = const()[name = string("const_268"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21803136)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_33 = conv(bias = const_268, dilations = conv2d_33_dilations_0, groups = conv2d_33_groups_0, pad = conv2d_33_pad_0, pad_type = conv2d_33_pad_type_0, strides = conv2d_33_strides_0, weight = const_267, x = relu_29)[name = string("_native_batch_norm_legit_no_training_33")];
+            tensor<fp32, [1, 256, 10, 377]> add_14 = add(x = _native_batch_norm_legit_no_training_33, y = relu_28)[name = string("add_14")];
+            tensor<fp32, [1, 256, 10, 377]> relu_30 = relu(x = add_14)[name = string("relu_30")];
+            string conv2d_34_pad_type_0 = const()[name = string("conv2d_34_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_34_pad_0 = const()[name = string("conv2d_34_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_34_strides_0 = const()[name = string("conv2d_34_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_34_dilations_0 = const()[name = string("conv2d_34_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_34_groups_0 = const()[name = string("conv2d_34_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_269 = const()[name = string("const_269"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21804224)))];
+            tensor<fp32, [256]> const_270 = const()[name = string("const_270"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24163584)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_34 = conv(bias = const_270, dilations = conv2d_34_dilations_0, groups = conv2d_34_groups_0, pad = conv2d_34_pad_0, pad_type = conv2d_34_pad_type_0, strides = conv2d_34_strides_0, weight = const_269, x = relu_30)[name = string("_native_batch_norm_legit_no_training_34")];
+            tensor<fp32, [1, 256, 10, 377]> relu_31 = relu(x = _native_batch_norm_legit_no_training_34)[name = string("relu_31")];
+            string conv2d_35_pad_type_0 = const()[name = string("conv2d_35_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> conv2d_35_pad_0 = const()[name = string("conv2d_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> conv2d_35_strides_0 = const()[name = string("conv2d_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> conv2d_35_dilations_0 = const()[name = string("conv2d_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 conv2d_35_groups_0 = const()[name = string("conv2d_35_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_271 = const()[name = string("const_271"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(24164672)))];
+            tensor<fp32, [256]> const_272 = const()[name = string("const_272"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26524032)))];
+            tensor<fp32, [1, 256, 10, 377]> _native_batch_norm_legit_no_training_35 = conv(bias = const_272, dilations = conv2d_35_dilations_0, groups = conv2d_35_groups_0, pad = conv2d_35_pad_0, pad_type = conv2d_35_pad_type_0, strides = conv2d_35_strides_0, weight = const_271, x = relu_31)[name = string("_native_batch_norm_legit_no_training_35")];
+            tensor<fp32, [1, 256, 10, 377]> add_15 = add(x = _native_batch_norm_legit_no_training_35, y = relu_30)[name = string("add_15")];
+            tensor<fp32, [1, 256, 10, 377]> relu_32 = relu(x = add_15)[name = string("relu_32")];
+            tensor<int32, [3]> const_179 = const()[name = string("const_179"), val = tensor<int32, [3]>([1, 2560, -1])];
+            tensor<fp32, [1, 2560, 377]> view = reshape(shape = const_179, x = relu_32)[name = string("view")];
+            tensor<int32, [1]> squeeze_axes_0 = const()[name = string("squeeze_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp32, [2560, 377]> squeeze = squeeze(axes = squeeze_axes_0, x = view)[name = string("squeeze")];
+            int32 index_select_batch_dims_0 = const()[name = string("index_select_batch_dims_0"), val = int32(0)];
+            bool index_select_validate_indices_0 = const()[name = string("index_select_validate_indices_0"), val = bool(false)];
+            tensor<int32, [2750]> select_0 = const()[name = string("select_0"), val = tensor<int32, [2750]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26525120)))];
+            int32 index_select_axis_0 = const()[name = string("index_select_axis_0"), val = int32(1)];
+            tensor<fp32, [2560, 2750]> index_select = gather(axis = index_select_axis_0, batch_dims = index_select_batch_dims_0, indices = select_0, validate_indices = index_select_validate_indices_0, x = squeeze)[name = string("index_select")];
+            tensor<int32, [3]> const_182 = const()[name = string("const_182"), val = tensor<int32, [3]>([2560, 22, 125])];
+            tensor<fp32, [2560, 22, 125]> view_1 = reshape(shape = const_182, x = index_select)[name = string("view_1")];
+            tensor<int32, [3]> const_183 = const()[name = string("const_183"), val = tensor<int32, [3]>([1, 0, 2])];
+            tensor<int32, [3]> tile_0_reps_0 = const()[name = string("tile_0_reps_0"), val = tensor<int32, [3]>([3, 1, 1])];
+            tensor<fp32, [22, 2560, 125]> permute_1 = transpose(perm = const_183, x = view_1)[name = string("transpose_2")];
+            tensor<fp32, [66, 2560, 125]> tile_0 = tile(reps = tile_0_reps_0, x = permute_1)[name = string("tile_0")];
+            tensor<int32, [4]> concat_0 = const()[name = string("concat_0"), val = tensor<int32, [4]>([3, 22, 2560, 125])];
+            tensor<fp32, [3, 22, 2560, 125]> reshape_0 = reshape(shape = concat_0, x = tile_0)[name = string("reshape_0")];
+            tensor<int32, [4]> transpose_0_perm_0 = const()[name = string("transpose_0_perm_0"), val = tensor<int32, [4]>([1, 0, 2, 3])];
+            tensor<int32, [3]> concat_1 = const()[name = string("concat_1"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [22, 3, 2560, 125]> transpose_0 = transpose(perm = transpose_0_perm_0, x = reshape_0)[name = string("transpose_1")];
+            tensor<fp32, [66, 2560, 125]> repeat_interleave = reshape(shape = concat_1, x = transpose_0)[name = string("repeat_interleave")];
+            tensor<int32, [1]> unsqueeze_1_axes_0 = const()[name = string("unsqueeze_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [66, 1, 589]> unsqueeze_1 = expand_dims(axes = unsqueeze_1_axes_0, x = masks)[name = string("unsqueeze_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [66, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = unsqueeze_1)[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, [66, 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]> upsample_nearest1d_axes_0 = const()[name = string("upsample_nearest1d_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [66, 1, 125]> upsample_nearest1d = squeeze(axes = upsample_nearest1d_axes_0, x = upsample_nearest_neighbor_0)[name = string("upsample_nearest1d")];
+            tensor<int32, [1]> sum_1_axes_0 = const()[name = string("sum_1_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_1_keep_dims_0 = const()[name = string("sum_1_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 1]> sum_1 = reduce_sum(axes = sum_1_axes_0, keep_dims = sum_1_keep_dims_0, x = upsample_nearest1d)[name = string("sum_1")];
+            fp32 const_189 = const()[name = string("const_189"), val = fp32(0x0p+0)];
+            tensor<bool, [66, 1]> gt = greater(x = sum_1, y = const_189)[name = string("gt")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [66, 1]> fill_like_0 = fill_like(ref_tensor = sum_1, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [66, 1]> where = select(a = sum_1, b = fill_like_0, cond = gt)[name = string("where")];
+            tensor<fp32, [66, 2560, 125]> mul = mul(x = repeat_interleave, y = upsample_nearest1d)[name = string("mul")];
+            tensor<int32, [1]> sum_2_axes_0 = const()[name = string("sum_2_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_2_keep_dims_0 = const()[name = string("sum_2_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 2560]> sum_2 = reduce_sum(axes = sum_2_axes_0, keep_dims = sum_2_keep_dims_0, x = mul)[name = string("sum_2")];
+            tensor<fp32, [66, 2560]> div = real_div(x = sum_2, y = where)[name = string("div")];
+            tensor<int32, [1]> unsqueeze_2_axes_0 = const()[name = string("unsqueeze_2_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [66, 2560, 1]> unsqueeze_2 = expand_dims(axes = unsqueeze_2_axes_0, x = div)[name = string("unsqueeze_2")];
+            tensor<fp32, [66, 2560, 125]> sub = sub(x = repeat_interleave, y = unsqueeze_2)[name = string("sub")];
+            tensor<fp32, [66, 2560, 125]> square = mul(x = sub, y = sub)[name = string("square")];
+            tensor<fp32, [66, 1, 125]> square_1 = mul(x = upsample_nearest1d, y = upsample_nearest1d)[name = string("square_1")];
+            tensor<int32, [1]> sum_3_axes_0 = const()[name = string("sum_3_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_3_keep_dims_0 = const()[name = string("sum_3_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 1]> sum_3 = reduce_sum(axes = sum_3_axes_0, keep_dims = sum_3_keep_dims_0, x = square_1)[name = string("sum_3")];
+            tensor<fp32, [66, 1]> div_1 = real_div(x = sum_3, y = where)[name = string("div_1")];
+            tensor<fp32, [66, 1]> sub_1 = sub(x = where, y = div_1)[name = string("sub_1")];
+            fp32 const_193 = const()[name = string("const_193"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [66, 1]> add_16 = add(x = sub_1, y = const_193)[name = string("add_16")];
+            tensor<fp32, [66, 2560, 125]> mul_1 = mul(x = square, y = upsample_nearest1d)[name = string("mul_1")];
+            tensor<int32, [1]> sum_4_axes_0 = const()[name = string("sum_4_axes_0"), val = tensor<int32, [1]>([2])];
+            bool sum_4_keep_dims_0 = const()[name = string("sum_4_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [66, 2560]> sum_4 = reduce_sum(axes = sum_4_axes_0, keep_dims = sum_4_keep_dims_0, x = mul_1)[name = string("sum_4")];
+            tensor<fp32, [66, 2560]> div_2 = real_div(x = sum_4, y = add_16)[name = string("div_2")];
+            fp32 const_195 = const()[name = string("const_195"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [66, 2560]> clamp_min = maximum(x = div_2, y = const_195)[name = string("clamp_min")];
+            tensor<fp32, [66, 2560]> sqrt = sqrt(x = clamp_min)[name = string("sqrt")];
+            int32 const_196 = const()[name = string("const_196"), val = int32(-1)];
+            bool cat_interleave_0 = const()[name = string("cat_interleave_0"), val = bool(false)];
+            tensor<fp32, [66, 5120]> cat = concat(axis = const_196, interleave = cat_interleave_0, values = (div, sqrt))[name = string("cat")];
+            tensor<fp32, [66, 2560]> zeros_like = const()[name = string("zeros_like"), val = tensor<fp32, [66, 2560]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26536192)))];
+            fp32 full_like_value_0 = const()[name = string("full_like_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [66, 2560]> full_like = fill_like(ref_tensor = sqrt, value = full_like_value_0)[name = string("full_like")];
+            int32 const_198 = const()[name = string("const_198"), val = int32(-1)];
+            bool cat_1_interleave_0 = const()[name = string("cat_1_interleave_0"), val = bool(false)];
+            tensor<fp32, [66, 5120]> cat_1 = concat(axis = const_198, interleave = cat_1_interleave_0, values = (zeros_like, full_like))[name = string("cat_1")];
+            fp32 const_199 = const()[name = string("const_199"), val = fp32(0x0p+0)];
+            tensor<bool, [66, 1]> le = less_equal(x = sum_1, y = const_199)[name = string("le")];
+            tensor<int32, [2]> const_200 = const()[name = string("const_200"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [66, 5120]> repeat = tile(reps = const_200, x = le)[name = string("repeat")];
+            tensor<fp32, [66, 5120]> where_1 = select(a = cat_1, b = cat, cond = repeat)[name = string("where_1")];
+            tensor<fp32, [66, 256]> output = linear(bias = p_resnet_seg_1_bias, weight = p_resnet_seg_1_weight, x = where_1)[name = string("linear_0")];
+        } -> (output);
+}

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