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plda_phi.npy

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+size 1152

wespeaker-fbank-b32.onnx

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wespeaker-fbank.onnx

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+oid sha256:fa699e2b9c43613ecbe7554239414c6b76e0e1c842476395d5c8aa15b773a74c
+size 110476

wespeaker-pool-classify-b3.mlmodelc/analytics/coremldata.bin

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+oid sha256:f8095941e4aa8e14246095978bab376580f5c2e946a511002bee0d8662776280
+size 243

wespeaker-pool-classify-b3.mlmodelc/coremldata.bin

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+oid sha256:4b5e1e6992cb7a316850fdd028c3e91d790f387acf26df8f756c83df293085d9
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wespeaker-pool-classify-b3.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, [?, 2560, 125]> frames, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"frames", [32, 2560, 125]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"5f146113", {{"frames", [3, 2560, 125]}, {"weights", [3, 589]}}}, {"9133f7d9", {{"frames", [32, 2560, 125]}, {"weights", [32, 589]}}}, {"c224ee3d", {{"frames", [1, 2560, 125]}, {"weights", [1, 589]}}}})))] {
+            tensor<fp32, [256]> resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [256, 5120]> resnet_seg_1_weight = const()[name = string("resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 589]> input_1 = expand_dims(axes = input_1_axes_0, x = weights)[name = string("input_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_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, [?, 1, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 125]> weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")];
+            fp32 var_16 = const()[name = string("op_16"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_17 = greater(x = weight_sum, y = var_16)[name = string("op_17")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1]> fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [?, 1]> safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_17)[name = string("safe_sum")];
+            tensor<fp32, [?, 2560, 125]> var_25 = mul(x = frames, y = weights_1)[name = string("op_25")];
+            tensor<int32, [1]> var_30_axes_0 = const()[name = string("op_30_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_30_keep_dims_0 = const()[name = string("op_30_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_30 = reduce_sum(axes = var_30_axes_0, keep_dims = var_30_keep_dims_0, x = var_25)[name = string("op_30")];
+            tensor<fp32, [?, 2560]> mean = real_div(x = var_30, y = safe_sum)[name = string("mean")];
+            tensor<int32, [1]> var_33_axes_0 = const()[name = string("op_33_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [?, 2560, 1]> var_33 = expand_dims(axes = var_33_axes_0, x = mean)[name = string("op_33")];
+            tensor<fp32, [?, 2560, 125]> var_35 = sub(x = frames, y = var_33)[name = string("op_35")];
+            tensor<fp32, [?, 2560, 125]> dx2 = mul(x = var_35, y = var_35)[name = string("dx2")];
+            tensor<fp32, [?, 1, 125]> var_37 = mul(x = weights_1, y = weights_1)[name = string("op_37")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_37)[name = string("weight_sq_sum")];
+            tensor<fp32, [?, 1]> var_43 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_43")];
+            tensor<fp32, [?, 1]> var_45 = sub(x = safe_sum, y = var_43)[name = string("op_45")];
+            fp32 var_47 = const()[name = string("op_47"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [?, 1]> denom = add(x = var_45, y = var_47)[name = string("denom")];
+            tensor<fp32, [?, 2560, 125]> var_49 = mul(x = dx2, y = weights_1)[name = string("op_49")];
+            tensor<int32, [1]> var_54_axes_0 = const()[name = string("op_54_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_54_keep_dims_0 = const()[name = string("op_54_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_54 = reduce_sum(axes = var_54_axes_0, keep_dims = var_54_keep_dims_0, x = var_49)[name = string("op_54")];
+            tensor<fp32, [?, 2560]> var = real_div(x = var_54, y = denom)[name = string("var")];
+            fp32 var_56 = const()[name = string("op_56"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [?, 2560]> var_57 = maximum(x = var, y = var_56)[name = string("op_57")];
+            tensor<fp32, [?, 2560]> std = sqrt(x = var_57)[name = string("std")];
+            int32 var_60 = const()[name = string("op_60"), val = int32(-1)];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> stats = concat(axis = var_60, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")];
+            tensor<fp32, [?, 2560]> var_67 = sub(x = mean, y = mean)[name = string("sub_0")];
+            fp32 var_74_value_0 = const()[name = string("op_74_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 2560]> var_74 = fill_like(ref_tensor = std, value = var_74_value_0)[name = string("op_74")];
+            int32 var_76 = const()[name = string("op_76"), val = int32(-1)];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> zero_stats = concat(axis = var_76, interleave = zero_stats_interleave_0, values = (var_67, var_74))[name = string("zero_stats")];
+            fp32 var_78 = const()[name = string("op_78"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_79 = less_equal(x = weight_sum, y = var_78)[name = string("op_79")];
+            tensor<int32, [2]> var_85 = const()[name = string("op_85"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_85, x = var_79)[name = string("zero_mask")];
+            tensor<fp32, [?, 5120]> input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")];
+            tensor<fp32, [?, 256]> output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight, x = input)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-pool-classify-b3.mlmodelc/weights/weight.bin

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wespeaker-pool-classify-b3.onnx

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+size 5344564

wespeaker-pool-classify-b32.mlmodelc/analytics/coremldata.bin

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wespeaker-pool-classify-b32.mlmodelc/coremldata.bin

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wespeaker-pool-classify-b32.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, [?, 2560, 125]> frames, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"frames", [32, 2560, 125]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"5f146113", {{"frames", [3, 2560, 125]}, {"weights", [3, 589]}}}, {"9133f7d9", {{"frames", [32, 2560, 125]}, {"weights", [32, 589]}}}, {"c224ee3d", {{"frames", [1, 2560, 125]}, {"weights", [1, 589]}}}})))] {
+            tensor<fp32, [256]> resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [256, 5120]> resnet_seg_1_weight = const()[name = string("resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 589]> input_1 = expand_dims(axes = input_1_axes_0, x = weights)[name = string("input_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_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, [?, 1, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 125]> weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")];
+            fp32 var_16 = const()[name = string("op_16"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_17 = greater(x = weight_sum, y = var_16)[name = string("op_17")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1]> fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [?, 1]> safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_17)[name = string("safe_sum")];
+            tensor<fp32, [?, 2560, 125]> var_25 = mul(x = frames, y = weights_1)[name = string("op_25")];
+            tensor<int32, [1]> var_30_axes_0 = const()[name = string("op_30_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_30_keep_dims_0 = const()[name = string("op_30_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_30 = reduce_sum(axes = var_30_axes_0, keep_dims = var_30_keep_dims_0, x = var_25)[name = string("op_30")];
+            tensor<fp32, [?, 2560]> mean = real_div(x = var_30, y = safe_sum)[name = string("mean")];
+            tensor<int32, [1]> var_33_axes_0 = const()[name = string("op_33_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [?, 2560, 1]> var_33 = expand_dims(axes = var_33_axes_0, x = mean)[name = string("op_33")];
+            tensor<fp32, [?, 2560, 125]> var_35 = sub(x = frames, y = var_33)[name = string("op_35")];
+            tensor<fp32, [?, 2560, 125]> dx2 = mul(x = var_35, y = var_35)[name = string("dx2")];
+            tensor<fp32, [?, 1, 125]> var_37 = mul(x = weights_1, y = weights_1)[name = string("op_37")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_37)[name = string("weight_sq_sum")];
+            tensor<fp32, [?, 1]> var_43 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_43")];
+            tensor<fp32, [?, 1]> var_45 = sub(x = safe_sum, y = var_43)[name = string("op_45")];
+            fp32 var_47 = const()[name = string("op_47"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [?, 1]> denom = add(x = var_45, y = var_47)[name = string("denom")];
+            tensor<fp32, [?, 2560, 125]> var_49 = mul(x = dx2, y = weights_1)[name = string("op_49")];
+            tensor<int32, [1]> var_54_axes_0 = const()[name = string("op_54_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_54_keep_dims_0 = const()[name = string("op_54_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_54 = reduce_sum(axes = var_54_axes_0, keep_dims = var_54_keep_dims_0, x = var_49)[name = string("op_54")];
+            tensor<fp32, [?, 2560]> var = real_div(x = var_54, y = denom)[name = string("var")];
+            fp32 var_56 = const()[name = string("op_56"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [?, 2560]> var_57 = maximum(x = var, y = var_56)[name = string("op_57")];
+            tensor<fp32, [?, 2560]> std = sqrt(x = var_57)[name = string("std")];
+            int32 var_60 = const()[name = string("op_60"), val = int32(-1)];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> stats = concat(axis = var_60, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")];
+            tensor<fp32, [?, 2560]> var_67 = sub(x = mean, y = mean)[name = string("sub_0")];
+            fp32 var_74_value_0 = const()[name = string("op_74_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 2560]> var_74 = fill_like(ref_tensor = std, value = var_74_value_0)[name = string("op_74")];
+            int32 var_76 = const()[name = string("op_76"), val = int32(-1)];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> zero_stats = concat(axis = var_76, interleave = zero_stats_interleave_0, values = (var_67, var_74))[name = string("zero_stats")];
+            fp32 var_78 = const()[name = string("op_78"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_79 = less_equal(x = weight_sum, y = var_78)[name = string("op_79")];
+            tensor<int32, [2]> var_85 = const()[name = string("op_85"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_85, x = var_79)[name = string("zero_mask")];
+            tensor<fp32, [?, 5120]> input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")];
+            tensor<fp32, [?, 256]> output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight, x = input)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-pool-classify-b32.mlmodelc/weights/weight.bin

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wespeaker-pool-classify-b32.onnx

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wespeaker-pool-classify.mlmodelc/analytics/coremldata.bin

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wespeaker-pool-classify.mlmodelc/coremldata.bin

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wespeaker-pool-classify.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, [?, 2560, 125]> frames, tensor<fp32, [?, 589]> weights) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"frames", [32, 2560, 125]}, {"weights", [32, 589]}}), ("EnumeratedShapes", {{"5f146113", {{"frames", [3, 2560, 125]}, {"weights", [3, 589]}}}, {"9133f7d9", {{"frames", [32, 2560, 125]}, {"weights", [32, 589]}}}, {"c224ee3d", {{"frames", [1, 2560, 125]}, {"weights", [1, 589]}}}})))] {
+            tensor<fp32, [256]> resnet_seg_1_bias = const()[name = string("resnet_seg_1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [256, 5120]> resnet_seg_1_weight = const()[name = string("resnet_seg_1_weight"), val = tensor<fp32, [256, 5120]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1152)))];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 1, 589]> input_1 = expand_dims(axes = input_1_axes_0, x = weights)[name = string("input_1")];
+            tensor<int32, [1]> expand_dims_0_axes_0 = const()[name = string("expand_dims_0_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 589, 1]> expand_dims_0 = expand_dims(axes = expand_dims_0_axes_0, x = input_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, [?, 1, 125, 1]> upsample_nearest_neighbor_0 = upsample_nearest_neighbor(scale_factor_height = upsample_nearest_neighbor_0_scale_factor_height_0, scale_factor_width = upsample_nearest_neighbor_0_scale_factor_width_0, x = expand_dims_0)[name = string("upsample_nearest_neighbor_0")];
+            tensor<int32, [1]> weights_axes_0 = const()[name = string("weights_axes_0"), val = tensor<int32, [1]>([3])];
+            tensor<fp32, [?, 1, 125]> weights_1 = squeeze(axes = weights_axes_0, x = upsample_nearest_neighbor_0)[name = string("weights")];
+            tensor<int32, [1]> weight_sum_axes_0 = const()[name = string("weight_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sum_keep_dims_0 = const()[name = string("weight_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sum = reduce_sum(axes = weight_sum_axes_0, keep_dims = weight_sum_keep_dims_0, x = weights_1)[name = string("weight_sum")];
+            fp32 var_16 = const()[name = string("op_16"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_17 = greater(x = weight_sum, y = var_16)[name = string("op_17")];
+            fp32 fill_like_0_value_0 = const()[name = string("fill_like_0_value_0"), val = fp32(0x1p+0)];
+            tensor<fp32, [?, 1]> fill_like_0 = fill_like(ref_tensor = weight_sum, value = fill_like_0_value_0)[name = string("fill_like_0")];
+            tensor<fp32, [?, 1]> safe_sum = select(a = weight_sum, b = fill_like_0, cond = var_17)[name = string("safe_sum")];
+            tensor<fp32, [?, 2560, 125]> var_25 = mul(x = frames, y = weights_1)[name = string("op_25")];
+            tensor<int32, [1]> var_30_axes_0 = const()[name = string("op_30_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_30_keep_dims_0 = const()[name = string("op_30_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_30 = reduce_sum(axes = var_30_axes_0, keep_dims = var_30_keep_dims_0, x = var_25)[name = string("op_30")];
+            tensor<fp32, [?, 2560]> mean = real_div(x = var_30, y = safe_sum)[name = string("mean")];
+            tensor<int32, [1]> var_33_axes_0 = const()[name = string("op_33_axes_0"), val = tensor<int32, [1]>([2])];
+            tensor<fp32, [?, 2560, 1]> var_33 = expand_dims(axes = var_33_axes_0, x = mean)[name = string("op_33")];
+            tensor<fp32, [?, 2560, 125]> var_35 = sub(x = frames, y = var_33)[name = string("op_35")];
+            tensor<fp32, [?, 2560, 125]> dx2 = mul(x = var_35, y = var_35)[name = string("dx2")];
+            tensor<fp32, [?, 1, 125]> var_37 = mul(x = weights_1, y = weights_1)[name = string("op_37")];
+            tensor<int32, [1]> weight_sq_sum_axes_0 = const()[name = string("weight_sq_sum_axes_0"), val = tensor<int32, [1]>([2])];
+            bool weight_sq_sum_keep_dims_0 = const()[name = string("weight_sq_sum_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 1]> weight_sq_sum = reduce_sum(axes = weight_sq_sum_axes_0, keep_dims = weight_sq_sum_keep_dims_0, x = var_37)[name = string("weight_sq_sum")];
+            tensor<fp32, [?, 1]> var_43 = real_div(x = weight_sq_sum, y = safe_sum)[name = string("op_43")];
+            tensor<fp32, [?, 1]> var_45 = sub(x = safe_sum, y = var_43)[name = string("op_45")];
+            fp32 var_47 = const()[name = string("op_47"), val = fp32(0x1.5798eep-27)];
+            tensor<fp32, [?, 1]> denom = add(x = var_45, y = var_47)[name = string("denom")];
+            tensor<fp32, [?, 2560, 125]> var_49 = mul(x = dx2, y = weights_1)[name = string("op_49")];
+            tensor<int32, [1]> var_54_axes_0 = const()[name = string("op_54_axes_0"), val = tensor<int32, [1]>([2])];
+            bool var_54_keep_dims_0 = const()[name = string("op_54_keep_dims_0"), val = bool(false)];
+            tensor<fp32, [?, 2560]> var_54 = reduce_sum(axes = var_54_axes_0, keep_dims = var_54_keep_dims_0, x = var_49)[name = string("op_54")];
+            tensor<fp32, [?, 2560]> var = real_div(x = var_54, y = denom)[name = string("var")];
+            fp32 var_56 = const()[name = string("op_56"), val = fp32(0x1.b7cdfep-34)];
+            tensor<fp32, [?, 2560]> var_57 = maximum(x = var, y = var_56)[name = string("op_57")];
+            tensor<fp32, [?, 2560]> std = sqrt(x = var_57)[name = string("std")];
+            int32 var_60 = const()[name = string("op_60"), val = int32(-1)];
+            bool stats_interleave_0 = const()[name = string("stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> stats = concat(axis = var_60, interleave = stats_interleave_0, values = (mean, std))[name = string("stats")];
+            tensor<fp32, [?, 2560]> var_67 = sub(x = mean, y = mean)[name = string("sub_0")];
+            fp32 var_74_value_0 = const()[name = string("op_74_value_0"), val = fp32(0x1.4f8b58p-17)];
+            tensor<fp32, [?, 2560]> var_74 = fill_like(ref_tensor = std, value = var_74_value_0)[name = string("op_74")];
+            int32 var_76 = const()[name = string("op_76"), val = int32(-1)];
+            bool zero_stats_interleave_0 = const()[name = string("zero_stats_interleave_0"), val = bool(false)];
+            tensor<fp32, [?, 5120]> zero_stats = concat(axis = var_76, interleave = zero_stats_interleave_0, values = (var_67, var_74))[name = string("zero_stats")];
+            fp32 var_78 = const()[name = string("op_78"), val = fp32(0x0p+0)];
+            tensor<bool, [?, 1]> var_79 = less_equal(x = weight_sum, y = var_78)[name = string("op_79")];
+            tensor<int32, [2]> var_85 = const()[name = string("op_85"), val = tensor<int32, [2]>([1, 5120])];
+            tensor<bool, [?, 5120]> zero_mask = tile(reps = var_85, x = var_79)[name = string("zero_mask")];
+            tensor<fp32, [?, 5120]> input = select(a = zero_stats, b = stats, cond = zero_mask)[name = string("input")];
+            tensor<fp32, [?, 256]> output = linear(bias = resnet_seg_1_bias, weight = resnet_seg_1_weight, x = input)[name = string("linear_0")];
+        } -> (output);
+}

wespeaker-pool-classify.mlmodelc/weights/weight.bin

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wespeaker-pool-classify.onnx

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wespeaker-resnet-frames-b32.mlmodelc/analytics/coremldata.bin

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+size 243

wespeaker-resnet-frames-b32.mlmodelc/coremldata.bin

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wespeaker-resnet-frames-b32.mlmodelc/model.mil

@@ -0,0 +1,349 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 998, 80]> fbank) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}}), ("EnumeratedShapes", {{"48583c1e", {{"fbank", [32, 998, 80]}}}, {"9ac8e6fe", {{"fbank", [1, 998, 80]}}}})))] {
+            tensor<int32, [3]> var_17 = const()[name = string("op_17"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 80, 998]> fbank_1 = transpose(perm = var_17, x = fbank)[name = string("transpose_0")];
+            tensor<fp32, [?, 1, 80, 998]> input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")];
+            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_0 = const()[name = string("const_0"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [32]> const_1 = const()[name = string("const_1"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1280)))];
+            tensor<fp32, [?, 32, 80, 998]> input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_7 = relu(x = input_5)[name = string("input_7")];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_2 = const()[name = string("const_2"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1472)))];
+            tensor<fp32, [32]> const_3 = const()[name = string("const_3"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38400)))];
+            tensor<fp32, [?, 32, 80, 998]> input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2, x = input_7)[name = string("input_11")];
+            tensor<fp32, [?, 32, 80, 998]> input_13 = relu(x = input_11)[name = string("input_13")];
+            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_4 = const()[name = string("const_4"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38592)))];
+            tensor<fp32, [32]> const_5 = const()[name = string("const_5"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75520)))];
+            tensor<fp32, [?, 32, 80, 998]> out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4, x = input_13)[name = string("out_1")];
+            tensor<fp32, [?, 32, 80, 998]> input_17 = add(x = out_1, y = input_7)[name = string("input_17")];
+            tensor<fp32, [?, 32, 80, 998]> input_19 = relu(x = input_17)[name = string("input_19")];
+            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_6 = const()[name = string("const_6"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75712)))];
+            tensor<fp32, [32]> const_7 = const()[name = string("const_7"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112640)))];
+            tensor<fp32, [?, 32, 80, 998]> input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6, x = input_19)[name = string("input_23")];
+            tensor<fp32, [?, 32, 80, 998]> input_25 = relu(x = input_23)[name = string("input_25")];
+            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112832)))];
+            tensor<fp32, [32]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149760)))];
+            tensor<fp32, [?, 32, 80, 998]> out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8, x = input_25)[name = string("out_3")];
+            tensor<fp32, [?, 32, 80, 998]> input_29 = add(x = out_3, y = input_19)[name = string("input_29")];
+            tensor<fp32, [?, 32, 80, 998]> input_31 = relu(x = input_29)[name = string("input_31")];
+            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149952)))];
+            tensor<fp32, [32]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186880)))];
+            tensor<fp32, [?, 32, 80, 998]> input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10, x = input_31)[name = string("input_35")];
+            tensor<fp32, [?, 32, 80, 998]> input_37 = relu(x = input_35)[name = string("input_37")];
+            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187072)))];
+            tensor<fp32, [32]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224000)))];
+            tensor<fp32, [?, 32, 80, 998]> out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12, x = input_37)[name = string("out_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_41 = add(x = out_5, y = input_31)[name = string("input_41")];
+            tensor<fp32, [?, 32, 80, 998]> input_43 = relu(x = input_41)[name = string("input_43")];
+            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224192)))];
+            tensor<fp32, [64]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297984)))];
+            tensor<fp32, [?, 64, 40, 499]> input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14, x = input_43)[name = string("input_47")];
+            tensor<fp32, [?, 64, 40, 499]> input_49 = relu(x = input_47)[name = string("input_49")];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298304)))];
+            tensor<fp32, [64]> const_17 = const()[name = string("const_17"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445824)))];
+            tensor<fp32, [?, 64, 40, 499]> out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16, x = input_49)[name = string("out_7")];
+            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_18 = const()[name = string("const_18"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446144)))];
+            tensor<fp32, [64]> const_19 = const()[name = string("const_19"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454400)))];
+            tensor<fp32, [?, 64, 40, 499]> var_191 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_191")];
+            tensor<fp32, [?, 64, 40, 499]> input_55 = add(x = out_7, y = var_191)[name = string("input_55")];
+            tensor<fp32, [?, 64, 40, 499]> input_57 = relu(x = input_55)[name = string("input_57")];
+            string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_20 = const()[name = string("const_20"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454720)))];
+            tensor<fp32, [64]> const_21 = const()[name = string("const_21"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(602240)))];
+            tensor<fp32, [?, 64, 40, 499]> input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20, x = input_57)[name = string("input_61")];
+            tensor<fp32, [?, 64, 40, 499]> input_63 = relu(x = input_61)[name = string("input_63")];
+            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_22 = const()[name = string("const_22"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(602560)))];
+            tensor<fp32, [64]> const_23 = const()[name = string("const_23"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(750080)))];
+            tensor<fp32, [?, 64, 40, 499]> out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22, x = input_63)[name = string("out_9")];
+            tensor<fp32, [?, 64, 40, 499]> input_67 = add(x = out_9, y = input_57)[name = string("input_67")];
+            tensor<fp32, [?, 64, 40, 499]> input_69 = relu(x = input_67)[name = string("input_69")];
+            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_24 = const()[name = string("const_24"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(750400)))];
+            tensor<fp32, [64]> const_25 = const()[name = string("const_25"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(897920)))];
+            tensor<fp32, [?, 64, 40, 499]> input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24, x = input_69)[name = string("input_73")];
+            tensor<fp32, [?, 64, 40, 499]> input_75 = relu(x = input_73)[name = string("input_75")];
+            string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(898240)))];
+            tensor<fp32, [64]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1045760)))];
+            tensor<fp32, [?, 64, 40, 499]> out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26, x = input_75)[name = string("out_11")];
+            tensor<fp32, [?, 64, 40, 499]> input_79 = add(x = out_11, y = input_69)[name = string("input_79")];
+            tensor<fp32, [?, 64, 40, 499]> input_81 = relu(x = input_79)[name = string("input_81")];
+            string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1046080)))];
+            tensor<fp32, [64]> const_29 = const()[name = string("const_29"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1193600)))];
+            tensor<fp32, [?, 64, 40, 499]> input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28, x = input_81)[name = string("input_85")];
+            tensor<fp32, [?, 64, 40, 499]> input_87 = relu(x = input_85)[name = string("input_87")];
+            string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_30 = const()[name = string("const_30"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1193920)))];
+            tensor<fp32, [64]> const_31 = const()[name = string("const_31"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1341440)))];
+            tensor<fp32, [?, 64, 40, 499]> out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30, x = input_87)[name = string("out_13")];
+            tensor<fp32, [?, 64, 40, 499]> input_91 = add(x = out_13, y = input_81)[name = string("input_91")];
+            tensor<fp32, [?, 64, 40, 499]> input_93 = relu(x = input_91)[name = string("input_93")];
+            string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_32 = const()[name = string("const_32"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1341760)))];
+            tensor<fp32, [128]> const_33 = const()[name = string("const_33"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1636736)))];
+            tensor<fp32, [?, 128, 20, 250]> input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32, x = input_93)[name = string("input_97")];
+            tensor<fp32, [?, 128, 20, 250]> input_99 = relu(x = input_97)[name = string("input_99")];
+            string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_34 = const()[name = string("const_34"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1637312)))];
+            tensor<fp32, [128]> const_35 = const()[name = string("const_35"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2227200)))];
+            tensor<fp32, [?, 128, 20, 250]> out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34, x = input_99)[name = string("out_15")];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_36 = const()[name = string("const_36"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2227776)))];
+            tensor<fp32, [128]> const_37 = const()[name = string("const_37"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2260608)))];
+            tensor<fp32, [?, 128, 20, 250]> var_335 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36, x = input_93)[name = string("op_335")];
+            tensor<fp32, [?, 128, 20, 250]> input_105 = add(x = out_15, y = var_335)[name = string("input_105")];
+            tensor<fp32, [?, 128, 20, 250]> input_107 = relu(x = input_105)[name = string("input_107")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_38 = const()[name = string("const_38"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2261184)))];
+            tensor<fp32, [128]> const_39 = const()[name = string("const_39"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2851072)))];
+            tensor<fp32, [?, 128, 20, 250]> input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38, x = input_107)[name = string("input_111")];
+            tensor<fp32, [?, 128, 20, 250]> input_113 = relu(x = input_111)[name = string("input_113")];
+            string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_40 = const()[name = string("const_40"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2851648)))];
+            tensor<fp32, [128]> const_41 = const()[name = string("const_41"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3441536)))];
+            tensor<fp32, [?, 128, 20, 250]> out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40, x = input_113)[name = string("out_17")];
+            tensor<fp32, [?, 128, 20, 250]> input_117 = add(x = out_17, y = input_107)[name = string("input_117")];
+            tensor<fp32, [?, 128, 20, 250]> input_119 = relu(x = input_117)[name = string("input_119")];
+            string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_42 = const()[name = string("const_42"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3442112)))];
+            tensor<fp32, [128]> const_43 = const()[name = string("const_43"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4032000)))];
+            tensor<fp32, [?, 128, 20, 250]> input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42, x = input_119)[name = string("input_123")];
+            tensor<fp32, [?, 128, 20, 250]> input_125 = relu(x = input_123)[name = string("input_125")];
+            string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_44 = const()[name = string("const_44"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4032576)))];
+            tensor<fp32, [128]> const_45 = const()[name = string("const_45"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4622464)))];
+            tensor<fp32, [?, 128, 20, 250]> out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44, x = input_125)[name = string("out_19")];
+            tensor<fp32, [?, 128, 20, 250]> input_129 = add(x = out_19, y = input_119)[name = string("input_129")];
+            tensor<fp32, [?, 128, 20, 250]> input_131 = relu(x = input_129)[name = string("input_131")];
+            string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_46 = const()[name = string("const_46"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4623040)))];
+            tensor<fp32, [128]> const_47 = const()[name = string("const_47"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5212928)))];
+            tensor<fp32, [?, 128, 20, 250]> input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46, x = input_131)[name = string("input_135")];
+            tensor<fp32, [?, 128, 20, 250]> input_137 = relu(x = input_135)[name = string("input_137")];
+            string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_48 = const()[name = string("const_48"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5213504)))];
+            tensor<fp32, [128]> const_49 = const()[name = string("const_49"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5803392)))];
+            tensor<fp32, [?, 128, 20, 250]> out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48, x = input_137)[name = string("out_21")];
+            tensor<fp32, [?, 128, 20, 250]> input_141 = add(x = out_21, y = input_131)[name = string("input_141")];
+            tensor<fp32, [?, 128, 20, 250]> input_143 = relu(x = input_141)[name = string("input_143")];
+            string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_50 = const()[name = string("const_50"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5803968)))];
+            tensor<fp32, [128]> const_51 = const()[name = string("const_51"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6393856)))];
+            tensor<fp32, [?, 128, 20, 250]> input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50, x = input_143)[name = string("input_147")];
+            tensor<fp32, [?, 128, 20, 250]> input_149 = relu(x = input_147)[name = string("input_149")];
+            string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_52 = const()[name = string("const_52"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6394432)))];
+            tensor<fp32, [128]> const_53 = const()[name = string("const_53"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6984320)))];
+            tensor<fp32, [?, 128, 20, 250]> out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52, x = input_149)[name = string("out_23")];
+            tensor<fp32, [?, 128, 20, 250]> input_153 = add(x = out_23, y = input_143)[name = string("input_153")];
+            tensor<fp32, [?, 128, 20, 250]> input_155 = relu(x = input_153)[name = string("input_155")];
+            string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_54 = const()[name = string("const_54"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6984896)))];
+            tensor<fp32, [128]> const_55 = const()[name = string("const_55"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7574784)))];
+            tensor<fp32, [?, 128, 20, 250]> input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54, x = input_155)[name = string("input_159")];
+            tensor<fp32, [?, 128, 20, 250]> input_161 = relu(x = input_159)[name = string("input_161")];
+            string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_56 = const()[name = string("const_56"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7575360)))];
+            tensor<fp32, [128]> const_57 = const()[name = string("const_57"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8165248)))];
+            tensor<fp32, [?, 128, 20, 250]> out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56, x = input_161)[name = string("out_25")];
+            tensor<fp32, [?, 128, 20, 250]> input_165 = add(x = out_25, y = input_155)[name = string("input_165")];
+            tensor<fp32, [?, 128, 20, 250]> input_167 = relu(x = input_165)[name = string("input_167")];
+            string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_58 = const()[name = string("const_58"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8165824)))];
+            tensor<fp32, [256]> const_59 = const()[name = string("const_59"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9345536)))];
+            tensor<fp32, [?, 256, 10, 125]> input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58, x = input_167)[name = string("input_171")];
+            tensor<fp32, [?, 256, 10, 125]> input_173 = relu(x = input_171)[name = string("input_173")];
+            string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_60 = const()[name = string("const_60"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9346624)))];
+            tensor<fp32, [256]> const_61 = const()[name = string("const_61"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11705984)))];
+            tensor<fp32, [?, 256, 10, 125]> out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60, x = input_173)[name = string("out_27")];
+            string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_62 = const()[name = string("const_62"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11707072)))];
+            tensor<fp32, [256]> const_63 = const()[name = string("const_63"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11838208)))];
+            tensor<fp32, [?, 256, 10, 125]> var_534 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62, x = input_167)[name = string("op_534")];
+            tensor<fp32, [?, 256, 10, 125]> input_179 = add(x = out_27, y = var_534)[name = string("input_179")];
+            tensor<fp32, [?, 256, 10, 125]> input_181 = relu(x = input_179)[name = string("input_181")];
+            string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_64 = const()[name = string("const_64"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11839296)))];
+            tensor<fp32, [256]> const_65 = const()[name = string("const_65"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14198656)))];
+            tensor<fp32, [?, 256, 10, 125]> input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64, x = input_181)[name = string("input_185")];
+            tensor<fp32, [?, 256, 10, 125]> input_187 = relu(x = input_185)[name = string("input_187")];
+            string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_66 = const()[name = string("const_66"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14199744)))];
+            tensor<fp32, [256]> const_67 = const()[name = string("const_67"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16559104)))];
+            tensor<fp32, [?, 256, 10, 125]> out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66, x = input_187)[name = string("out_29")];
+            tensor<fp32, [?, 256, 10, 125]> input_191 = add(x = out_29, y = input_181)[name = string("input_191")];
+            tensor<fp32, [?, 256, 10, 125]> input_193 = relu(x = input_191)[name = string("input_193")];
+            string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_68 = const()[name = string("const_68"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16560192)))];
+            tensor<fp32, [256]> const_69 = const()[name = string("const_69"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18919552)))];
+            tensor<fp32, [?, 256, 10, 125]> input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68, x = input_193)[name = string("input_197")];
+            tensor<fp32, [?, 256, 10, 125]> input_199 = relu(x = input_197)[name = string("input_199")];
+            string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_70 = const()[name = string("const_70"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18920640)))];
+            tensor<fp32, [256]> const_71 = const()[name = string("const_71"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21280000)))];
+            tensor<fp32, [?, 256, 10, 125]> out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70, x = input_199)[name = string("out")];
+            tensor<fp32, [?, 256, 10, 125]> input = add(x = out, y = input_193)[name = string("input")];
+            tensor<fp32, [?, 256, 10, 125]> frames = relu(x = input)[name = string("frames")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [?, 2560, 125]> output = reshape(shape = concat_0x, x = frames)[name = string("op_612")];
+        } -> (output);
+}

wespeaker-resnet-frames-b32.mlmodelc/weights/weight.bin

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+version https://git-lfs.github.com/spec/v1
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+size 21281088

wespeaker-resnet-frames-b32.onnx

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+size 21450468

wespeaker-resnet-frames.mlmodelc/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0c660a62c67a382c10d86c909ec278734382357f850da69fc385dc94301e5d20
+size 243

wespeaker-resnet-frames.mlmodelc/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:622be39055413728e82ccf1cf9fd4ea8cbcc6a7bf104482fb2d7e9a1e3559170
+size 162

wespeaker-resnet-frames.mlmodelc/model.mil

@@ -0,0 +1,349 @@
+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3505.4.1"}})]
+{
+    func main<ios18>(tensor<fp32, [?, 998, 80]> fbank) [FlexibleShapeInformation = tuple<tuple<string, dict<string, tensor<int32, [?]>>>, tuple<string, dict<string, dict<string, tensor<int32, [?]>>>>>((("DefaultShapes", {{"fbank", [32, 998, 80]}}), ("EnumeratedShapes", {{"48583c1e", {{"fbank", [32, 998, 80]}}}, {"9ac8e6fe", {{"fbank", [1, 998, 80]}}}})))] {
+            tensor<int32, [3]> var_17 = const()[name = string("op_17"), val = tensor<int32, [3]>([0, 2, 1])];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([1])];
+            tensor<fp32, [?, 80, 998]> fbank_1 = transpose(perm = var_17, x = fbank)[name = string("transpose_0")];
+            tensor<fp32, [?, 1, 80, 998]> input_1 = expand_dims(axes = input_1_axes_0, x = fbank_1)[name = string("input_1")];
+            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 1, 3, 3]> const_0 = const()[name = string("const_0"), val = tensor<fp32, [32, 1, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp32, [32]> const_1 = const()[name = string("const_1"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1280)))];
+            tensor<fp32, [?, 32, 80, 998]> input_5 = conv(bias = const_1, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = const_0, x = input_1)[name = string("input_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_7 = relu(x = input_5)[name = string("input_7")];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_2 = const()[name = string("const_2"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1472)))];
+            tensor<fp32, [32]> const_3 = const()[name = string("const_3"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38400)))];
+            tensor<fp32, [?, 32, 80, 998]> input_11 = conv(bias = const_3, dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = const_2, x = input_7)[name = string("input_11")];
+            tensor<fp32, [?, 32, 80, 998]> input_13 = relu(x = input_11)[name = string("input_13")];
+            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_4 = const()[name = string("const_4"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(38592)))];
+            tensor<fp32, [32]> const_5 = const()[name = string("const_5"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75520)))];
+            tensor<fp32, [?, 32, 80, 998]> out_1 = conv(bias = const_5, dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = const_4, x = input_13)[name = string("out_1")];
+            tensor<fp32, [?, 32, 80, 998]> input_17 = add(x = out_1, y = input_7)[name = string("input_17")];
+            tensor<fp32, [?, 32, 80, 998]> input_19 = relu(x = input_17)[name = string("input_19")];
+            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_6 = const()[name = string("const_6"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(75712)))];
+            tensor<fp32, [32]> const_7 = const()[name = string("const_7"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112640)))];
+            tensor<fp32, [?, 32, 80, 998]> input_23 = conv(bias = const_7, dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = const_6, x = input_19)[name = string("input_23")];
+            tensor<fp32, [?, 32, 80, 998]> input_25 = relu(x = input_23)[name = string("input_25")];
+            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_8 = const()[name = string("const_8"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(112832)))];
+            tensor<fp32, [32]> const_9 = const()[name = string("const_9"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149760)))];
+            tensor<fp32, [?, 32, 80, 998]> out_3 = conv(bias = const_9, dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = const_8, x = input_25)[name = string("out_3")];
+            tensor<fp32, [?, 32, 80, 998]> input_29 = add(x = out_3, y = input_19)[name = string("input_29")];
+            tensor<fp32, [?, 32, 80, 998]> input_31 = relu(x = input_29)[name = string("input_31")];
+            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_10 = const()[name = string("const_10"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(149952)))];
+            tensor<fp32, [32]> const_11 = const()[name = string("const_11"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186880)))];
+            tensor<fp32, [?, 32, 80, 998]> input_35 = conv(bias = const_11, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = const_10, x = input_31)[name = string("input_35")];
+            tensor<fp32, [?, 32, 80, 998]> input_37 = relu(x = input_35)[name = string("input_37")];
+            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
+            tensor<fp32, [32, 32, 3, 3]> const_12 = const()[name = string("const_12"), val = tensor<fp32, [32, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(187072)))];
+            tensor<fp32, [32]> const_13 = const()[name = string("const_13"), val = tensor<fp32, [32]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224000)))];
+            tensor<fp32, [?, 32, 80, 998]> out_5 = conv(bias = const_13, dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = const_12, x = input_37)[name = string("out_5")];
+            tensor<fp32, [?, 32, 80, 998]> input_41 = add(x = out_5, y = input_31)[name = string("input_41")];
+            tensor<fp32, [?, 32, 80, 998]> input_43 = relu(x = input_41)[name = string("input_43")];
+            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 3, 3]> const_14 = const()[name = string("const_14"), val = tensor<fp32, [64, 32, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(224192)))];
+            tensor<fp32, [64]> const_15 = const()[name = string("const_15"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(297984)))];
+            tensor<fp32, [?, 64, 40, 499]> input_47 = conv(bias = const_15, dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = const_14, x = input_43)[name = string("input_47")];
+            tensor<fp32, [?, 64, 40, 499]> input_49 = relu(x = input_47)[name = string("input_49")];
+            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_16 = const()[name = string("const_16"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(298304)))];
+            tensor<fp32, [64]> const_17 = const()[name = string("const_17"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(445824)))];
+            tensor<fp32, [?, 64, 40, 499]> out_7 = conv(bias = const_17, dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = const_16, x = input_49)[name = string("out_7")];
+            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 32, 1, 1]> const_18 = const()[name = string("const_18"), val = tensor<fp32, [64, 32, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(446144)))];
+            tensor<fp32, [64]> const_19 = const()[name = string("const_19"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454400)))];
+            tensor<fp32, [?, 64, 40, 499]> var_191 = conv(bias = const_19, dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = const_18, x = input_43)[name = string("op_191")];
+            tensor<fp32, [?, 64, 40, 499]> input_55 = add(x = out_7, y = var_191)[name = string("input_55")];
+            tensor<fp32, [?, 64, 40, 499]> input_57 = relu(x = input_55)[name = string("input_57")];
+            string input_59_pad_type_0 = const()[name = string("input_59_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_59_pad_0 = const()[name = string("input_59_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_59_strides_0 = const()[name = string("input_59_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_59_dilations_0 = const()[name = string("input_59_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_59_groups_0 = const()[name = string("input_59_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_20 = const()[name = string("const_20"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454720)))];
+            tensor<fp32, [64]> const_21 = const()[name = string("const_21"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(602240)))];
+            tensor<fp32, [?, 64, 40, 499]> input_61 = conv(bias = const_21, dilations = input_59_dilations_0, groups = input_59_groups_0, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = input_59_strides_0, weight = const_20, x = input_57)[name = string("input_61")];
+            tensor<fp32, [?, 64, 40, 499]> input_63 = relu(x = input_61)[name = string("input_63")];
+            string input_65_pad_type_0 = const()[name = string("input_65_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_65_pad_0 = const()[name = string("input_65_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_65_strides_0 = const()[name = string("input_65_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_65_dilations_0 = const()[name = string("input_65_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_65_groups_0 = const()[name = string("input_65_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_22 = const()[name = string("const_22"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(602560)))];
+            tensor<fp32, [64]> const_23 = const()[name = string("const_23"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(750080)))];
+            tensor<fp32, [?, 64, 40, 499]> out_9 = conv(bias = const_23, dilations = input_65_dilations_0, groups = input_65_groups_0, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = input_65_strides_0, weight = const_22, x = input_63)[name = string("out_9")];
+            tensor<fp32, [?, 64, 40, 499]> input_67 = add(x = out_9, y = input_57)[name = string("input_67")];
+            tensor<fp32, [?, 64, 40, 499]> input_69 = relu(x = input_67)[name = string("input_69")];
+            string input_71_pad_type_0 = const()[name = string("input_71_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_71_pad_0 = const()[name = string("input_71_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_71_strides_0 = const()[name = string("input_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_71_dilations_0 = const()[name = string("input_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_71_groups_0 = const()[name = string("input_71_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_24 = const()[name = string("const_24"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(750400)))];
+            tensor<fp32, [64]> const_25 = const()[name = string("const_25"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(897920)))];
+            tensor<fp32, [?, 64, 40, 499]> input_73 = conv(bias = const_25, dilations = input_71_dilations_0, groups = input_71_groups_0, pad = input_71_pad_0, pad_type = input_71_pad_type_0, strides = input_71_strides_0, weight = const_24, x = input_69)[name = string("input_73")];
+            tensor<fp32, [?, 64, 40, 499]> input_75 = relu(x = input_73)[name = string("input_75")];
+            string input_77_pad_type_0 = const()[name = string("input_77_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_77_pad_0 = const()[name = string("input_77_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_77_strides_0 = const()[name = string("input_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_77_dilations_0 = const()[name = string("input_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_77_groups_0 = const()[name = string("input_77_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_26 = const()[name = string("const_26"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(898240)))];
+            tensor<fp32, [64]> const_27 = const()[name = string("const_27"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1045760)))];
+            tensor<fp32, [?, 64, 40, 499]> out_11 = conv(bias = const_27, dilations = input_77_dilations_0, groups = input_77_groups_0, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = input_77_strides_0, weight = const_26, x = input_75)[name = string("out_11")];
+            tensor<fp32, [?, 64, 40, 499]> input_79 = add(x = out_11, y = input_69)[name = string("input_79")];
+            tensor<fp32, [?, 64, 40, 499]> input_81 = relu(x = input_79)[name = string("input_81")];
+            string input_83_pad_type_0 = const()[name = string("input_83_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_83_pad_0 = const()[name = string("input_83_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_83_strides_0 = const()[name = string("input_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_83_dilations_0 = const()[name = string("input_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_83_groups_0 = const()[name = string("input_83_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_28 = const()[name = string("const_28"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1046080)))];
+            tensor<fp32, [64]> const_29 = const()[name = string("const_29"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1193600)))];
+            tensor<fp32, [?, 64, 40, 499]> input_85 = conv(bias = const_29, dilations = input_83_dilations_0, groups = input_83_groups_0, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = input_83_strides_0, weight = const_28, x = input_81)[name = string("input_85")];
+            tensor<fp32, [?, 64, 40, 499]> input_87 = relu(x = input_85)[name = string("input_87")];
+            string input_89_pad_type_0 = const()[name = string("input_89_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_89_pad_0 = const()[name = string("input_89_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_89_strides_0 = const()[name = string("input_89_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_89_dilations_0 = const()[name = string("input_89_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_89_groups_0 = const()[name = string("input_89_groups_0"), val = int32(1)];
+            tensor<fp32, [64, 64, 3, 3]> const_30 = const()[name = string("const_30"), val = tensor<fp32, [64, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1193920)))];
+            tensor<fp32, [64]> const_31 = const()[name = string("const_31"), val = tensor<fp32, [64]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1341440)))];
+            tensor<fp32, [?, 64, 40, 499]> out_13 = conv(bias = const_31, dilations = input_89_dilations_0, groups = input_89_groups_0, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = input_89_strides_0, weight = const_30, x = input_87)[name = string("out_13")];
+            tensor<fp32, [?, 64, 40, 499]> input_91 = add(x = out_13, y = input_81)[name = string("input_91")];
+            tensor<fp32, [?, 64, 40, 499]> input_93 = relu(x = input_91)[name = string("input_93")];
+            string input_95_pad_type_0 = const()[name = string("input_95_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_95_pad_0 = const()[name = string("input_95_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_95_strides_0 = const()[name = string("input_95_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_95_dilations_0 = const()[name = string("input_95_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_95_groups_0 = const()[name = string("input_95_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 3, 3]> const_32 = const()[name = string("const_32"), val = tensor<fp32, [128, 64, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1341760)))];
+            tensor<fp32, [128]> const_33 = const()[name = string("const_33"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1636736)))];
+            tensor<fp32, [?, 128, 20, 250]> input_97 = conv(bias = const_33, dilations = input_95_dilations_0, groups = input_95_groups_0, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = input_95_strides_0, weight = const_32, x = input_93)[name = string("input_97")];
+            tensor<fp32, [?, 128, 20, 250]> input_99 = relu(x = input_97)[name = string("input_99")];
+            string input_101_pad_type_0 = const()[name = string("input_101_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_101_pad_0 = const()[name = string("input_101_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_101_strides_0 = const()[name = string("input_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_101_dilations_0 = const()[name = string("input_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_101_groups_0 = const()[name = string("input_101_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_34 = const()[name = string("const_34"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1637312)))];
+            tensor<fp32, [128]> const_35 = const()[name = string("const_35"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2227200)))];
+            tensor<fp32, [?, 128, 20, 250]> out_15 = conv(bias = const_35, dilations = input_101_dilations_0, groups = input_101_groups_0, pad = input_101_pad_0, pad_type = input_101_pad_type_0, strides = input_101_strides_0, weight = const_34, x = input_99)[name = string("out_15")];
+            string input_103_pad_type_0 = const()[name = string("input_103_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_103_strides_0 = const()[name = string("input_103_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_103_pad_0 = const()[name = string("input_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_103_dilations_0 = const()[name = string("input_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_103_groups_0 = const()[name = string("input_103_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 64, 1, 1]> const_36 = const()[name = string("const_36"), val = tensor<fp32, [128, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2227776)))];
+            tensor<fp32, [128]> const_37 = const()[name = string("const_37"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2260608)))];
+            tensor<fp32, [?, 128, 20, 250]> var_335 = conv(bias = const_37, dilations = input_103_dilations_0, groups = input_103_groups_0, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = input_103_strides_0, weight = const_36, x = input_93)[name = string("op_335")];
+            tensor<fp32, [?, 128, 20, 250]> input_105 = add(x = out_15, y = var_335)[name = string("input_105")];
+            tensor<fp32, [?, 128, 20, 250]> input_107 = relu(x = input_105)[name = string("input_107")];
+            string input_109_pad_type_0 = const()[name = string("input_109_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_109_pad_0 = const()[name = string("input_109_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_109_strides_0 = const()[name = string("input_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_109_dilations_0 = const()[name = string("input_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_109_groups_0 = const()[name = string("input_109_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_38 = const()[name = string("const_38"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2261184)))];
+            tensor<fp32, [128]> const_39 = const()[name = string("const_39"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2851072)))];
+            tensor<fp32, [?, 128, 20, 250]> input_111 = conv(bias = const_39, dilations = input_109_dilations_0, groups = input_109_groups_0, pad = input_109_pad_0, pad_type = input_109_pad_type_0, strides = input_109_strides_0, weight = const_38, x = input_107)[name = string("input_111")];
+            tensor<fp32, [?, 128, 20, 250]> input_113 = relu(x = input_111)[name = string("input_113")];
+            string input_115_pad_type_0 = const()[name = string("input_115_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_115_pad_0 = const()[name = string("input_115_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_115_strides_0 = const()[name = string("input_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_115_dilations_0 = const()[name = string("input_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_115_groups_0 = const()[name = string("input_115_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_40 = const()[name = string("const_40"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2851648)))];
+            tensor<fp32, [128]> const_41 = const()[name = string("const_41"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3441536)))];
+            tensor<fp32, [?, 128, 20, 250]> out_17 = conv(bias = const_41, dilations = input_115_dilations_0, groups = input_115_groups_0, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = input_115_strides_0, weight = const_40, x = input_113)[name = string("out_17")];
+            tensor<fp32, [?, 128, 20, 250]> input_117 = add(x = out_17, y = input_107)[name = string("input_117")];
+            tensor<fp32, [?, 128, 20, 250]> input_119 = relu(x = input_117)[name = string("input_119")];
+            string input_121_pad_type_0 = const()[name = string("input_121_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_121_pad_0 = const()[name = string("input_121_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_121_strides_0 = const()[name = string("input_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_121_dilations_0 = const()[name = string("input_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_121_groups_0 = const()[name = string("input_121_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_42 = const()[name = string("const_42"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3442112)))];
+            tensor<fp32, [128]> const_43 = const()[name = string("const_43"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4032000)))];
+            tensor<fp32, [?, 128, 20, 250]> input_123 = conv(bias = const_43, dilations = input_121_dilations_0, groups = input_121_groups_0, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = input_121_strides_0, weight = const_42, x = input_119)[name = string("input_123")];
+            tensor<fp32, [?, 128, 20, 250]> input_125 = relu(x = input_123)[name = string("input_125")];
+            string input_127_pad_type_0 = const()[name = string("input_127_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_127_pad_0 = const()[name = string("input_127_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_127_strides_0 = const()[name = string("input_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_127_dilations_0 = const()[name = string("input_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_127_groups_0 = const()[name = string("input_127_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_44 = const()[name = string("const_44"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4032576)))];
+            tensor<fp32, [128]> const_45 = const()[name = string("const_45"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4622464)))];
+            tensor<fp32, [?, 128, 20, 250]> out_19 = conv(bias = const_45, dilations = input_127_dilations_0, groups = input_127_groups_0, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = input_127_strides_0, weight = const_44, x = input_125)[name = string("out_19")];
+            tensor<fp32, [?, 128, 20, 250]> input_129 = add(x = out_19, y = input_119)[name = string("input_129")];
+            tensor<fp32, [?, 128, 20, 250]> input_131 = relu(x = input_129)[name = string("input_131")];
+            string input_133_pad_type_0 = const()[name = string("input_133_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_133_pad_0 = const()[name = string("input_133_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_133_strides_0 = const()[name = string("input_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_133_dilations_0 = const()[name = string("input_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_133_groups_0 = const()[name = string("input_133_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_46 = const()[name = string("const_46"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4623040)))];
+            tensor<fp32, [128]> const_47 = const()[name = string("const_47"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5212928)))];
+            tensor<fp32, [?, 128, 20, 250]> input_135 = conv(bias = const_47, dilations = input_133_dilations_0, groups = input_133_groups_0, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = input_133_strides_0, weight = const_46, x = input_131)[name = string("input_135")];
+            tensor<fp32, [?, 128, 20, 250]> input_137 = relu(x = input_135)[name = string("input_137")];
+            string input_139_pad_type_0 = const()[name = string("input_139_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_139_pad_0 = const()[name = string("input_139_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_139_strides_0 = const()[name = string("input_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_139_dilations_0 = const()[name = string("input_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_139_groups_0 = const()[name = string("input_139_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_48 = const()[name = string("const_48"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5213504)))];
+            tensor<fp32, [128]> const_49 = const()[name = string("const_49"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5803392)))];
+            tensor<fp32, [?, 128, 20, 250]> out_21 = conv(bias = const_49, dilations = input_139_dilations_0, groups = input_139_groups_0, pad = input_139_pad_0, pad_type = input_139_pad_type_0, strides = input_139_strides_0, weight = const_48, x = input_137)[name = string("out_21")];
+            tensor<fp32, [?, 128, 20, 250]> input_141 = add(x = out_21, y = input_131)[name = string("input_141")];
+            tensor<fp32, [?, 128, 20, 250]> input_143 = relu(x = input_141)[name = string("input_143")];
+            string input_145_pad_type_0 = const()[name = string("input_145_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_145_pad_0 = const()[name = string("input_145_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_145_strides_0 = const()[name = string("input_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_145_dilations_0 = const()[name = string("input_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_145_groups_0 = const()[name = string("input_145_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_50 = const()[name = string("const_50"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(5803968)))];
+            tensor<fp32, [128]> const_51 = const()[name = string("const_51"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6393856)))];
+            tensor<fp32, [?, 128, 20, 250]> input_147 = conv(bias = const_51, dilations = input_145_dilations_0, groups = input_145_groups_0, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = input_145_strides_0, weight = const_50, x = input_143)[name = string("input_147")];
+            tensor<fp32, [?, 128, 20, 250]> input_149 = relu(x = input_147)[name = string("input_149")];
+            string input_151_pad_type_0 = const()[name = string("input_151_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_151_pad_0 = const()[name = string("input_151_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_151_strides_0 = const()[name = string("input_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_151_dilations_0 = const()[name = string("input_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_151_groups_0 = const()[name = string("input_151_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_52 = const()[name = string("const_52"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6394432)))];
+            tensor<fp32, [128]> const_53 = const()[name = string("const_53"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6984320)))];
+            tensor<fp32, [?, 128, 20, 250]> out_23 = conv(bias = const_53, dilations = input_151_dilations_0, groups = input_151_groups_0, pad = input_151_pad_0, pad_type = input_151_pad_type_0, strides = input_151_strides_0, weight = const_52, x = input_149)[name = string("out_23")];
+            tensor<fp32, [?, 128, 20, 250]> input_153 = add(x = out_23, y = input_143)[name = string("input_153")];
+            tensor<fp32, [?, 128, 20, 250]> input_155 = relu(x = input_153)[name = string("input_155")];
+            string input_157_pad_type_0 = const()[name = string("input_157_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_157_pad_0 = const()[name = string("input_157_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_157_strides_0 = const()[name = string("input_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_157_dilations_0 = const()[name = string("input_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_157_groups_0 = const()[name = string("input_157_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_54 = const()[name = string("const_54"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6984896)))];
+            tensor<fp32, [128]> const_55 = const()[name = string("const_55"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7574784)))];
+            tensor<fp32, [?, 128, 20, 250]> input_159 = conv(bias = const_55, dilations = input_157_dilations_0, groups = input_157_groups_0, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = input_157_strides_0, weight = const_54, x = input_155)[name = string("input_159")];
+            tensor<fp32, [?, 128, 20, 250]> input_161 = relu(x = input_159)[name = string("input_161")];
+            string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)];
+            tensor<fp32, [128, 128, 3, 3]> const_56 = const()[name = string("const_56"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(7575360)))];
+            tensor<fp32, [128]> const_57 = const()[name = string("const_57"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8165248)))];
+            tensor<fp32, [?, 128, 20, 250]> out_25 = conv(bias = const_57, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = const_56, x = input_161)[name = string("out_25")];
+            tensor<fp32, [?, 128, 20, 250]> input_165 = add(x = out_25, y = input_155)[name = string("input_165")];
+            tensor<fp32, [?, 128, 20, 250]> input_167 = relu(x = input_165)[name = string("input_167")];
+            string input_169_pad_type_0 = const()[name = string("input_169_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_169_pad_0 = const()[name = string("input_169_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_169_strides_0 = const()[name = string("input_169_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> input_169_dilations_0 = const()[name = string("input_169_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_169_groups_0 = const()[name = string("input_169_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 3, 3]> const_58 = const()[name = string("const_58"), val = tensor<fp32, [256, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8165824)))];
+            tensor<fp32, [256]> const_59 = const()[name = string("const_59"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9345536)))];
+            tensor<fp32, [?, 256, 10, 125]> input_171 = conv(bias = const_59, dilations = input_169_dilations_0, groups = input_169_groups_0, pad = input_169_pad_0, pad_type = input_169_pad_type_0, strides = input_169_strides_0, weight = const_58, x = input_167)[name = string("input_171")];
+            tensor<fp32, [?, 256, 10, 125]> input_173 = relu(x = input_171)[name = string("input_173")];
+            string input_175_pad_type_0 = const()[name = string("input_175_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_175_pad_0 = const()[name = string("input_175_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_175_strides_0 = const()[name = string("input_175_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_175_dilations_0 = const()[name = string("input_175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_175_groups_0 = const()[name = string("input_175_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_60 = const()[name = string("const_60"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9346624)))];
+            tensor<fp32, [256]> const_61 = const()[name = string("const_61"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11705984)))];
+            tensor<fp32, [?, 256, 10, 125]> out_27 = conv(bias = const_61, dilations = input_175_dilations_0, groups = input_175_groups_0, pad = input_175_pad_0, pad_type = input_175_pad_type_0, strides = input_175_strides_0, weight = const_60, x = input_173)[name = string("out_27")];
+            string input_177_pad_type_0 = const()[name = string("input_177_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_177_strides_0 = const()[name = string("input_177_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [4]> input_177_pad_0 = const()[name = string("input_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> input_177_dilations_0 = const()[name = string("input_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_177_groups_0 = const()[name = string("input_177_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 128, 1, 1]> const_62 = const()[name = string("const_62"), val = tensor<fp32, [256, 128, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11707072)))];
+            tensor<fp32, [256]> const_63 = const()[name = string("const_63"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11838208)))];
+            tensor<fp32, [?, 256, 10, 125]> var_534 = conv(bias = const_63, dilations = input_177_dilations_0, groups = input_177_groups_0, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = input_177_strides_0, weight = const_62, x = input_167)[name = string("op_534")];
+            tensor<fp32, [?, 256, 10, 125]> input_179 = add(x = out_27, y = var_534)[name = string("input_179")];
+            tensor<fp32, [?, 256, 10, 125]> input_181 = relu(x = input_179)[name = string("input_181")];
+            string input_183_pad_type_0 = const()[name = string("input_183_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_183_pad_0 = const()[name = string("input_183_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_183_strides_0 = const()[name = string("input_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_183_dilations_0 = const()[name = string("input_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_183_groups_0 = const()[name = string("input_183_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_64 = const()[name = string("const_64"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11839296)))];
+            tensor<fp32, [256]> const_65 = const()[name = string("const_65"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14198656)))];
+            tensor<fp32, [?, 256, 10, 125]> input_185 = conv(bias = const_65, dilations = input_183_dilations_0, groups = input_183_groups_0, pad = input_183_pad_0, pad_type = input_183_pad_type_0, strides = input_183_strides_0, weight = const_64, x = input_181)[name = string("input_185")];
+            tensor<fp32, [?, 256, 10, 125]> input_187 = relu(x = input_185)[name = string("input_187")];
+            string input_189_pad_type_0 = const()[name = string("input_189_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_189_pad_0 = const()[name = string("input_189_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_189_strides_0 = const()[name = string("input_189_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_189_dilations_0 = const()[name = string("input_189_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_189_groups_0 = const()[name = string("input_189_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_66 = const()[name = string("const_66"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(14199744)))];
+            tensor<fp32, [256]> const_67 = const()[name = string("const_67"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16559104)))];
+            tensor<fp32, [?, 256, 10, 125]> out_29 = conv(bias = const_67, dilations = input_189_dilations_0, groups = input_189_groups_0, pad = input_189_pad_0, pad_type = input_189_pad_type_0, strides = input_189_strides_0, weight = const_66, x = input_187)[name = string("out_29")];
+            tensor<fp32, [?, 256, 10, 125]> input_191 = add(x = out_29, y = input_181)[name = string("input_191")];
+            tensor<fp32, [?, 256, 10, 125]> input_193 = relu(x = input_191)[name = string("input_193")];
+            string input_195_pad_type_0 = const()[name = string("input_195_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_195_pad_0 = const()[name = string("input_195_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_195_strides_0 = const()[name = string("input_195_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_195_dilations_0 = const()[name = string("input_195_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_195_groups_0 = const()[name = string("input_195_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_68 = const()[name = string("const_68"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16560192)))];
+            tensor<fp32, [256]> const_69 = const()[name = string("const_69"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18919552)))];
+            tensor<fp32, [?, 256, 10, 125]> input_197 = conv(bias = const_69, dilations = input_195_dilations_0, groups = input_195_groups_0, pad = input_195_pad_0, pad_type = input_195_pad_type_0, strides = input_195_strides_0, weight = const_68, x = input_193)[name = string("input_197")];
+            tensor<fp32, [?, 256, 10, 125]> input_199 = relu(x = input_197)[name = string("input_199")];
+            string input_201_pad_type_0 = const()[name = string("input_201_pad_type_0"), val = string("custom")];
+            tensor<int32, [4]> input_201_pad_0 = const()[name = string("input_201_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
+            tensor<int32, [2]> input_201_strides_0 = const()[name = string("input_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> input_201_dilations_0 = const()[name = string("input_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 input_201_groups_0 = const()[name = string("input_201_groups_0"), val = int32(1)];
+            tensor<fp32, [256, 256, 3, 3]> const_70 = const()[name = string("const_70"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18920640)))];
+            tensor<fp32, [256]> const_71 = const()[name = string("const_71"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21280000)))];
+            tensor<fp32, [?, 256, 10, 125]> out = conv(bias = const_71, dilations = input_201_dilations_0, groups = input_201_groups_0, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = input_201_strides_0, weight = const_70, x = input_199)[name = string("out")];
+            tensor<fp32, [?, 256, 10, 125]> input = add(x = out, y = input_193)[name = string("input")];
+            tensor<fp32, [?, 256, 10, 125]> frames = relu(x = input)[name = string("frames")];
+            tensor<int32, [3]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [3]>([-1, 2560, 125])];
+            tensor<fp32, [?, 2560, 125]> output = reshape(shape = concat_0x, x = frames)[name = string("op_612")];
+        } -> (output);
+}

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