Upload converted Core ML artifacts

README.md

@@ -0,0 +1,23 @@
+# Converted Models
+
+This directory is the local staging area for converted runtime model artifacts.
+
+Layout:
+
+- `coreml/`: `.mlpackage` outputs produced by the local MediaPipe conversion pipeline.
+- `compiled/`: compiled `.mlmodelc` directories produced by `xcrun coremlcompiler`.
+- `intermediate/`: optional SavedModel and protobuf conversion outputs. This directory is ignored by git.
+
+The binary model artifacts in this tree are intentionally ignored by git. They are meant to be:
+
+1. generated locally for validation,
+2. uploaded to a Hugging Face model repository,
+3. pulled back down for development as needed.
+
+Typical workflow:
+
+```sh
+just convert-models all
+just upload-models your-org/wormhole-mediapipe-coreml
+just download-models your-org/wormhole-mediapipe-coreml
+```

compiled/face_detection_short_range.mlmodelc/analytics/coremldata.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bc234a62a6f35f4ea2808ee81d5f2a42d81b625799792c8f35e00c62e545f4bd
+size 243

compiled/face_detection_short_range.mlmodelc/coremldata.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6d13a825d4114de9825c2b6682ce53632ff8cdd5a49f7832a230e941be7a97d
+size 295

compiled/face_detection_short_range.mlmodelc/metadata.json

@@ -0,0 +1,77 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 896 × 1)",
+        "shortDescription" : "",
+        "shape" : "[1, 896, 1]",
+        "name" : "classificators",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 896 × 16)",
+        "shortDescription" : "",
+        "shape" : "[1, 896, 16]",
+        "name" : "regressors",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 6,
+    "mlProgramOperationTypeHistogram" : {
+      "Concat" : 2,
+      "Cast" : 3,
+      "Conv" : 37,
+      "Pad" : 11,
+      "Relu" : 17,
+      "Transpose" : 5,
+      "Add" : 16,
+      "Reshape" : 4,
+      "MaxPool" : 3
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "12.0",
+      "tvOS" : "15.0",
+      "visionOS" : "1.0",
+      "watchOS" : "8.0",
+      "iOS" : "15.0",
+      "macCatalyst" : "15.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.version" : "8.3.0",
+      "com.github.apple.coremltools.source" : "tensorflow==2.16.2"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 128 × 128 × 3)",
+        "shortDescription" : "",
+        "shape" : "[1, 128, 128, 3]",
+        "name" : "input",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "face_detection_short_range",
+    "method" : "predict"
+  }
+]

compiled/face_detection_short_range.mlmodelc/model.mil

@@ -0,0 +1,411 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-tensorflow", "2.16.2"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios15>(tensor<fp32, [1, 128, 128, 3]> input) {
+            tensor<int32, [3]> reshape_shape = const()[name = tensor<string, []>("reshape_shape"), val = tensor<int32, [3]>([1, -1, 1])];
+            tensor<int32, [3]> reshape_2_shape = const()[name = tensor<string, []>("reshape_2_shape"), val = tensor<int32, [3]>([1, -1, 1])];
+            tensor<int32, []> classificators_axis = const()[name = tensor<string, []>("classificators_axis"), val = tensor<int32, []>(1)];
+            tensor<int32, [3]> reshape_1_shape = const()[name = tensor<string, []>("reshape_1_shape"), val = tensor<int32, [3]>([1, -1, 16])];
+            tensor<int32, [3]> reshape_3_shape = const()[name = tensor<string, []>("reshape_3_shape"), val = tensor<int32, [3]>([1, -1, 16])];
+            tensor<int32, []> regressors_axis = const()[name = tensor<string, []>("regressors_axis"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> transpose_1_perm_0 = const()[name = tensor<string, []>("transpose_1_perm_0"), val = tensor<int32, [4]>([0, 3, 1, 2])];
+            tensor<string, []> input_to_fp16_dtype_0 = const()[name = tensor<string, []>("input_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<string, []> Conv2Dx_pad_type_0 = const()[name = tensor<string, []>("Conv2Dx_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> Conv2Dx_strides_0 = const()[name = tensor<string, []>("Conv2Dx_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> Conv2Dx_dilations_0 = const()[name = tensor<string, []>("Conv2Dx_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2Dx_groups_0 = const()[name = tensor<string, []>("Conv2Dx_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2Dx_pad_0 = const()[name = tensor<string, []>("Conv2Dx_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [24, 3, 5, 5]> transpose_0_to_fp16 = const()[name = tensor<string, []>("transpose_0_to_fp16"), val = tensor<fp16, [24, 3, 5, 5]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [24]> const_39_to_fp16 = const()[name = tensor<string, []>("const_39_to_fp16"), val = tensor<fp16, [24]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3776)))];
+            tensor<fp16, [1, 128, 128, 3]> input_to_fp16 = cast(dtype = input_to_fp16_dtype_0, x = input)[name = tensor<string, []>("cast_13")];
+            tensor<fp16, [1, 3, 128, 128]> transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_to_fp16)[name = tensor<string, []>("transpose_81")];
+            tensor<fp16, [1, 24, 64, 64]> conv2d_1_cast_fp16 = conv(bias = const_39_to_fp16, dilations = Conv2Dx_dilations_0, groups = Conv2Dx_groups_0, pad = Conv2Dx_pad_0, pad_type = Conv2Dx_pad_type_0, strides = Conv2Dx_strides_0, weight = transpose_0_to_fp16, x = transpose_1_cast_fp16)[name = tensor<string, []>("conv2d_1_cast_fp16")];
+            tensor<fp16, [1, 24, 64, 64]> activation_cast_fp16 = relu(x = conv2d_1_cast_fp16)[name = tensor<string, []>("activation_cast_fp16")];
+            tensor<string, []> depthwisex_pad_type_0 = const()[name = tensor<string, []>("depthwisex_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwisex_strides_0 = const()[name = tensor<string, []>("depthwisex_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwisex_dilations_0 = const()[name = tensor<string, []>("depthwisex_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwisex_groups_0 = const()[name = tensor<string, []>("depthwisex_groups_0"), val = tensor<int32, []>(24)];
+            tensor<int32, [4]> depthwisex_pad_0 = const()[name = tensor<string, []>("depthwisex_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [24, 1, 3, 3]> transpose_2_to_fp16 = const()[name = tensor<string, []>("transpose_2_to_fp16"), val = tensor<fp16, [24, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3904)))];
+            tensor<fp16, [1, 24, 64, 64]> depthwisex_cast_fp16 = conv(dilations = depthwisex_dilations_0, groups = depthwisex_groups_0, pad = depthwisex_pad_0, pad_type = depthwisex_pad_type_0, strides = depthwisex_strides_0, weight = transpose_2_to_fp16, x = activation_cast_fp16)[name = tensor<string, []>("depthwisex_cast_fp16")];
+            tensor<string, []> Conv2D_2x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_2x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_2x_strides_0 = const()[name = tensor<string, []>("Conv2D_2x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_2x_dilations_0 = const()[name = tensor<string, []>("Conv2D_2x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_2x_groups_0 = const()[name = tensor<string, []>("Conv2D_2x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_2x_pad_0 = const()[name = tensor<string, []>("Conv2D_2x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [24, 24, 1, 1]> transpose_4_to_fp16 = const()[name = tensor<string, []>("transpose_4_to_fp16"), val = tensor<fp16, [24, 24, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4416)))];
+            tensor<fp16, [24]> const_40_to_fp16 = const()[name = tensor<string, []>("const_40_to_fp16"), val = tensor<fp16, [24]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5632)))];
+            tensor<fp16, [1, 24, 64, 64]> conv2d_1_1_cast_fp16 = conv(bias = const_40_to_fp16, dilations = Conv2D_2x_dilations_0, groups = Conv2D_2x_groups_0, pad = Conv2D_2x_pad_0, pad_type = Conv2D_2x_pad_type_0, strides = Conv2D_2x_strides_0, weight = transpose_4_to_fp16, x = depthwisex_cast_fp16)[name = tensor<string, []>("conv2d_1_1_cast_fp16")];
+            tensor<fp16, [1, 24, 64, 64]> add__xeno_compat__1_cast_fp16 = add(x = activation_cast_fp16, y = conv2d_1_1_cast_fp16)[name = tensor<string, []>("add__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 24, 64, 64]> activation_1_cast_fp16 = relu(x = add__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_1_cast_fp16")];
+            tensor<string, []> pad_0_mode_0 = const()[name = tensor<string, []>("pad_0_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_12 = const()[name = tensor<string, []>("const_12"), val = tensor<int32, [8]>([0, 0, 0, 4, 0, 0, 0, 0])];
+            tensor<fp16, []> const_0_to_fp16 = const()[name = tensor<string, []>("const_0_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 28, 64, 64]> pad_0_cast_fp16 = pad(constant_val = const_0_to_fp16, mode = pad_0_mode_0, pad = const_12, x = activation_1_cast_fp16)[name = tensor<string, []>("pad_0_cast_fp16")];
+            tensor<string, []> depthwise_1x_pad_type_0 = const()[name = tensor<string, []>("depthwise_1x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_1x_strides_0 = const()[name = tensor<string, []>("depthwise_1x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_1x_dilations_0 = const()[name = tensor<string, []>("depthwise_1x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_1x_groups_0 = const()[name = tensor<string, []>("depthwise_1x_groups_0"), val = tensor<int32, []>(24)];
+            tensor<int32, [4]> depthwise_1x_pad_0 = const()[name = tensor<string, []>("depthwise_1x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [24, 1, 3, 3]> transpose_6_to_fp16 = const()[name = tensor<string, []>("transpose_6_to_fp16"), val = tensor<fp16, [24, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5760)))];
+            tensor<fp16, [1, 24, 64, 64]> depthwise_1x_cast_fp16 = conv(dilations = depthwise_1x_dilations_0, groups = depthwise_1x_groups_0, pad = depthwise_1x_pad_0, pad_type = depthwise_1x_pad_type_0, strides = depthwise_1x_strides_0, weight = transpose_6_to_fp16, x = activation_1_cast_fp16)[name = tensor<string, []>("depthwise_1x_cast_fp16")];
+            tensor<string, []> Conv2D_3x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_3x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_3x_strides_0 = const()[name = tensor<string, []>("Conv2D_3x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_3x_dilations_0 = const()[name = tensor<string, []>("Conv2D_3x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_3x_groups_0 = const()[name = tensor<string, []>("Conv2D_3x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_3x_pad_0 = const()[name = tensor<string, []>("Conv2D_3x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [28, 24, 1, 1]> transpose_8_to_fp16 = const()[name = tensor<string, []>("transpose_8_to_fp16"), val = tensor<fp16, [28, 24, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6272)))];
+            tensor<fp16, [28]> const_41_to_fp16 = const()[name = tensor<string, []>("const_41_to_fp16"), val = tensor<fp16, [28]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7680)))];
+            tensor<fp16, [1, 28, 64, 64]> conv2d_2_1_cast_fp16 = conv(bias = const_41_to_fp16, dilations = Conv2D_3x_dilations_0, groups = Conv2D_3x_groups_0, pad = Conv2D_3x_pad_0, pad_type = Conv2D_3x_pad_type_0, strides = Conv2D_3x_strides_0, weight = transpose_8_to_fp16, x = depthwise_1x_cast_fp16)[name = tensor<string, []>("conv2d_2_1_cast_fp16")];
+            tensor<fp16, [1, 28, 64, 64]> add_1__xeno_compat__1_cast_fp16 = add(x = pad_0_cast_fp16, y = conv2d_2_1_cast_fp16)[name = tensor<string, []>("add_1__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 28, 64, 64]> activation_2_cast_fp16 = relu(x = add_1__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_2_cast_fp16")];
+            tensor<int32, [2]> max_pool_0_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_0_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_0_strides_0 = const()[name = tensor<string, []>("max_pool_0_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_0_pad_type_0 = const()[name = tensor<string, []>("max_pool_0_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [4]> max_pool_0_pad_0 = const()[name = tensor<string, []>("max_pool_0_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_0_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_0_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 28, 32, 32]> max_pool_0_cast_fp16 = max_pool(ceil_mode = max_pool_0_ceil_mode_0, kernel_sizes = max_pool_0_kernel_sizes_0, pad = max_pool_0_pad_0, pad_type = max_pool_0_pad_type_0, strides = max_pool_0_strides_0, x = activation_2_cast_fp16)[name = tensor<string, []>("max_pool_0_cast_fp16")];
+            tensor<string, []> depthwise_2x_pad_type_0 = const()[name = tensor<string, []>("depthwise_2x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_2x_strides_0 = const()[name = tensor<string, []>("depthwise_2x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_2x_dilations_0 = const()[name = tensor<string, []>("depthwise_2x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_2x_groups_0 = const()[name = tensor<string, []>("depthwise_2x_groups_0"), val = tensor<int32, []>(28)];
+            tensor<int32, [4]> depthwise_2x_pad_0 = const()[name = tensor<string, []>("depthwise_2x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [28, 1, 3, 3]> transpose_11_to_fp16 = const()[name = tensor<string, []>("transpose_11_to_fp16"), val = tensor<fp16, [28, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7808)))];
+            tensor<fp16, [1, 28, 32, 32]> depthwise_2x_cast_fp16 = conv(dilations = depthwise_2x_dilations_0, groups = depthwise_2x_groups_0, pad = depthwise_2x_pad_0, pad_type = depthwise_2x_pad_type_0, strides = depthwise_2x_strides_0, weight = transpose_11_to_fp16, x = activation_2_cast_fp16)[name = tensor<string, []>("depthwise_2x_cast_fp16")];
+            tensor<string, []> pad_1_mode_0 = const()[name = tensor<string, []>("pad_1_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_18 = const()[name = tensor<string, []>("const_18"), val = tensor<int32, [8]>([0, 0, 0, 4, 0, 0, 0, 0])];
+            tensor<fp16, []> const_1_to_fp16 = const()[name = tensor<string, []>("const_1_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 32, 32, 32]> pad_1_cast_fp16 = pad(constant_val = const_1_to_fp16, mode = pad_1_mode_0, pad = const_18, x = max_pool_0_cast_fp16)[name = tensor<string, []>("pad_1_cast_fp16")];
+            tensor<string, []> Conv2D_4x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_4x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_4x_strides_0 = const()[name = tensor<string, []>("Conv2D_4x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_4x_dilations_0 = const()[name = tensor<string, []>("Conv2D_4x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_4x_groups_0 = const()[name = tensor<string, []>("Conv2D_4x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_4x_pad_0 = const()[name = tensor<string, []>("Conv2D_4x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 28, 1, 1]> transpose_13_to_fp16 = const()[name = tensor<string, []>("transpose_13_to_fp16"), val = tensor<fp16, [32, 28, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8384)))];
+            tensor<fp16, [32]> const_42_to_fp16 = const()[name = tensor<string, []>("const_42_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10240)))];
+            tensor<fp16, [1, 32, 32, 32]> conv2d_3_1_cast_fp16 = conv(bias = const_42_to_fp16, dilations = Conv2D_4x_dilations_0, groups = Conv2D_4x_groups_0, pad = Conv2D_4x_pad_0, pad_type = Conv2D_4x_pad_type_0, strides = Conv2D_4x_strides_0, weight = transpose_13_to_fp16, x = depthwise_2x_cast_fp16)[name = tensor<string, []>("conv2d_3_1_cast_fp16")];
+            tensor<fp16, [1, 32, 32, 32]> add_2__xeno_compat__1_cast_fp16 = add(x = pad_1_cast_fp16, y = conv2d_3_1_cast_fp16)[name = tensor<string, []>("add_2__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 32, 32, 32]> activation_3_cast_fp16 = relu(x = add_2__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_3_cast_fp16")];
+            tensor<string, []> pad_2_mode_0 = const()[name = tensor<string, []>("pad_2_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_16 = const()[name = tensor<string, []>("const_16"), val = tensor<int32, [8]>([0, 0, 0, 4, 0, 0, 0, 0])];
+            tensor<fp16, []> const_2_to_fp16 = const()[name = tensor<string, []>("const_2_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 36, 32, 32]> pad_2_cast_fp16 = pad(constant_val = const_2_to_fp16, mode = pad_2_mode_0, pad = const_16, x = activation_3_cast_fp16)[name = tensor<string, []>("pad_2_cast_fp16")];
+            tensor<string, []> depthwise_3x_pad_type_0 = const()[name = tensor<string, []>("depthwise_3x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_3x_strides_0 = const()[name = tensor<string, []>("depthwise_3x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_3x_dilations_0 = const()[name = tensor<string, []>("depthwise_3x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_3x_groups_0 = const()[name = tensor<string, []>("depthwise_3x_groups_0"), val = tensor<int32, []>(32)];
+            tensor<int32, [4]> depthwise_3x_pad_0 = const()[name = tensor<string, []>("depthwise_3x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 1, 3, 3]> transpose_15_to_fp16 = const()[name = tensor<string, []>("transpose_15_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10368)))];
+            tensor<fp16, [1, 32, 32, 32]> depthwise_3x_cast_fp16 = conv(dilations = depthwise_3x_dilations_0, groups = depthwise_3x_groups_0, pad = depthwise_3x_pad_0, pad_type = depthwise_3x_pad_type_0, strides = depthwise_3x_strides_0, weight = transpose_15_to_fp16, x = activation_3_cast_fp16)[name = tensor<string, []>("depthwise_3x_cast_fp16")];
+            tensor<string, []> Conv2D_5x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_5x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_5x_strides_0 = const()[name = tensor<string, []>("Conv2D_5x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_5x_dilations_0 = const()[name = tensor<string, []>("Conv2D_5x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_5x_groups_0 = const()[name = tensor<string, []>("Conv2D_5x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_5x_pad_0 = const()[name = tensor<string, []>("Conv2D_5x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [36, 32, 1, 1]> transpose_17_to_fp16 = const()[name = tensor<string, []>("transpose_17_to_fp16"), val = tensor<fp16, [36, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11008)))];
+            tensor<fp16, [36]> const_43_to_fp16 = const()[name = tensor<string, []>("const_43_to_fp16"), val = tensor<fp16, [36]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13376)))];
+            tensor<fp16, [1, 36, 32, 32]> conv2d_4_1_cast_fp16 = conv(bias = const_43_to_fp16, dilations = Conv2D_5x_dilations_0, groups = Conv2D_5x_groups_0, pad = Conv2D_5x_pad_0, pad_type = Conv2D_5x_pad_type_0, strides = Conv2D_5x_strides_0, weight = transpose_17_to_fp16, x = depthwise_3x_cast_fp16)[name = tensor<string, []>("conv2d_4_1_cast_fp16")];
+            tensor<fp16, [1, 36, 32, 32]> add_3__xeno_compat__1_cast_fp16 = add(x = pad_2_cast_fp16, y = conv2d_4_1_cast_fp16)[name = tensor<string, []>("add_3__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 36, 32, 32]> activation_4_cast_fp16 = relu(x = add_3__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_4_cast_fp16")];
+            tensor<string, []> pad_3_mode_0 = const()[name = tensor<string, []>("pad_3_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_17 = const()[name = tensor<string, []>("const_17"), val = tensor<int32, [8]>([0, 0, 0, 6, 0, 0, 0, 0])];
+            tensor<fp16, []> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 42, 32, 32]> pad_3_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = pad_3_mode_0, pad = const_17, x = activation_4_cast_fp16)[name = tensor<string, []>("pad_3_cast_fp16")];
+            tensor<string, []> depthwise_4x_pad_type_0 = const()[name = tensor<string, []>("depthwise_4x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_4x_strides_0 = const()[name = tensor<string, []>("depthwise_4x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_4x_dilations_0 = const()[name = tensor<string, []>("depthwise_4x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_4x_groups_0 = const()[name = tensor<string, []>("depthwise_4x_groups_0"), val = tensor<int32, []>(36)];
+            tensor<int32, [4]> depthwise_4x_pad_0 = const()[name = tensor<string, []>("depthwise_4x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [36, 1, 3, 3]> transpose_19_to_fp16 = const()[name = tensor<string, []>("transpose_19_to_fp16"), val = tensor<fp16, [36, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(13568)))];
+            tensor<fp16, [1, 36, 32, 32]> depthwise_4x_cast_fp16 = conv(dilations = depthwise_4x_dilations_0, groups = depthwise_4x_groups_0, pad = depthwise_4x_pad_0, pad_type = depthwise_4x_pad_type_0, strides = depthwise_4x_strides_0, weight = transpose_19_to_fp16, x = activation_4_cast_fp16)[name = tensor<string, []>("depthwise_4x_cast_fp16")];
+            tensor<string, []> Conv2D_6x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_6x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_6x_strides_0 = const()[name = tensor<string, []>("Conv2D_6x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_6x_dilations_0 = const()[name = tensor<string, []>("Conv2D_6x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_6x_groups_0 = const()[name = tensor<string, []>("Conv2D_6x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_6x_pad_0 = const()[name = tensor<string, []>("Conv2D_6x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [42, 36, 1, 1]> transpose_21_to_fp16 = const()[name = tensor<string, []>("transpose_21_to_fp16"), val = tensor<fp16, [42, 36, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14336)))];
+            tensor<fp16, [42]> const_44_to_fp16 = const()[name = tensor<string, []>("const_44_to_fp16"), val = tensor<fp16, [42]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17472)))];
+            tensor<fp16, [1, 42, 32, 32]> conv2d_5_1_cast_fp16 = conv(bias = const_44_to_fp16, dilations = Conv2D_6x_dilations_0, groups = Conv2D_6x_groups_0, pad = Conv2D_6x_pad_0, pad_type = Conv2D_6x_pad_type_0, strides = Conv2D_6x_strides_0, weight = transpose_21_to_fp16, x = depthwise_4x_cast_fp16)[name = tensor<string, []>("conv2d_5_1_cast_fp16")];
+            tensor<fp16, [1, 42, 32, 32]> add_4__xeno_compat__1_cast_fp16 = add(x = pad_3_cast_fp16, y = conv2d_5_1_cast_fp16)[name = tensor<string, []>("add_4__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 42, 32, 32]> activation_5_cast_fp16 = relu(x = add_4__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_5_cast_fp16")];
+            tensor<int32, [2]> max_pool_1_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_1_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_1_strides_0 = const()[name = tensor<string, []>("max_pool_1_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_1_pad_type_0 = const()[name = tensor<string, []>("max_pool_1_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [4]> max_pool_1_pad_0 = const()[name = tensor<string, []>("max_pool_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_1_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_1_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 42, 16, 16]> max_pool_1_cast_fp16 = max_pool(ceil_mode = max_pool_1_ceil_mode_0, kernel_sizes = max_pool_1_kernel_sizes_0, pad = max_pool_1_pad_0, pad_type = max_pool_1_pad_type_0, strides = max_pool_1_strides_0, x = activation_5_cast_fp16)[name = tensor<string, []>("max_pool_1_cast_fp16")];
+            tensor<string, []> depthwise_5x_pad_type_0 = const()[name = tensor<string, []>("depthwise_5x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_5x_strides_0 = const()[name = tensor<string, []>("depthwise_5x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_5x_dilations_0 = const()[name = tensor<string, []>("depthwise_5x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_5x_groups_0 = const()[name = tensor<string, []>("depthwise_5x_groups_0"), val = tensor<int32, []>(42)];
+            tensor<int32, [4]> depthwise_5x_pad_0 = const()[name = tensor<string, []>("depthwise_5x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [42, 1, 3, 3]> transpose_24_to_fp16 = const()[name = tensor<string, []>("transpose_24_to_fp16"), val = tensor<fp16, [42, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17664)))];
+            tensor<fp16, [1, 42, 16, 16]> depthwise_5x_cast_fp16 = conv(dilations = depthwise_5x_dilations_0, groups = depthwise_5x_groups_0, pad = depthwise_5x_pad_0, pad_type = depthwise_5x_pad_type_0, strides = depthwise_5x_strides_0, weight = transpose_24_to_fp16, x = activation_5_cast_fp16)[name = tensor<string, []>("depthwise_5x_cast_fp16")];
+            tensor<string, []> pad_4_mode_0 = const()[name = tensor<string, []>("pad_4_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_21 = const()[name = tensor<string, []>("const_21"), val = tensor<int32, [8]>([0, 0, 0, 6, 0, 0, 0, 0])];
+            tensor<fp16, []> const_4_to_fp16 = const()[name = tensor<string, []>("const_4_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 48, 16, 16]> pad_4_cast_fp16 = pad(constant_val = const_4_to_fp16, mode = pad_4_mode_0, pad = const_21, x = max_pool_1_cast_fp16)[name = tensor<string, []>("pad_4_cast_fp16")];
+            tensor<string, []> Conv2D_7x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_7x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_7x_strides_0 = const()[name = tensor<string, []>("Conv2D_7x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_7x_dilations_0 = const()[name = tensor<string, []>("Conv2D_7x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_7x_groups_0 = const()[name = tensor<string, []>("Conv2D_7x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_7x_pad_0 = const()[name = tensor<string, []>("Conv2D_7x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [48, 42, 1, 1]> transpose_26_to_fp16 = const()[name = tensor<string, []>("transpose_26_to_fp16"), val = tensor<fp16, [48, 42, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18496)))];
+            tensor<fp16, [48]> const_45_to_fp16 = const()[name = tensor<string, []>("const_45_to_fp16"), val = tensor<fp16, [48]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22592)))];
+            tensor<fp16, [1, 48, 16, 16]> conv2d_6_1_cast_fp16 = conv(bias = const_45_to_fp16, dilations = Conv2D_7x_dilations_0, groups = Conv2D_7x_groups_0, pad = Conv2D_7x_pad_0, pad_type = Conv2D_7x_pad_type_0, strides = Conv2D_7x_strides_0, weight = transpose_26_to_fp16, x = depthwise_5x_cast_fp16)[name = tensor<string, []>("conv2d_6_1_cast_fp16")];
+            tensor<fp16, [1, 48, 16, 16]> add_5__xeno_compat__1_cast_fp16 = add(x = pad_4_cast_fp16, y = conv2d_6_1_cast_fp16)[name = tensor<string, []>("add_5__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 48, 16, 16]> activation_6_cast_fp16 = relu(x = add_5__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_6_cast_fp16")];
+            tensor<string, []> pad_5_mode_0 = const()[name = tensor<string, []>("pad_5_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_22 = const()[name = tensor<string, []>("const_22"), val = tensor<int32, [8]>([0, 0, 0, 8, 0, 0, 0, 0])];
+            tensor<fp16, []> const_5_to_fp16 = const()[name = tensor<string, []>("const_5_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 56, 16, 16]> pad_5_cast_fp16 = pad(constant_val = const_5_to_fp16, mode = pad_5_mode_0, pad = const_22, x = activation_6_cast_fp16)[name = tensor<string, []>("pad_5_cast_fp16")];
+            tensor<string, []> depthwise_6x_pad_type_0 = const()[name = tensor<string, []>("depthwise_6x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_6x_strides_0 = const()[name = tensor<string, []>("depthwise_6x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_6x_dilations_0 = const()[name = tensor<string, []>("depthwise_6x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_6x_groups_0 = const()[name = tensor<string, []>("depthwise_6x_groups_0"), val = tensor<int32, []>(48)];
+            tensor<int32, [4]> depthwise_6x_pad_0 = const()[name = tensor<string, []>("depthwise_6x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [48, 1, 3, 3]> transpose_28_to_fp16 = const()[name = tensor<string, []>("transpose_28_to_fp16"), val = tensor<fp16, [48, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(22784)))];
+            tensor<fp16, [1, 48, 16, 16]> depthwise_6x_cast_fp16 = conv(dilations = depthwise_6x_dilations_0, groups = depthwise_6x_groups_0, pad = depthwise_6x_pad_0, pad_type = depthwise_6x_pad_type_0, strides = depthwise_6x_strides_0, weight = transpose_28_to_fp16, x = activation_6_cast_fp16)[name = tensor<string, []>("depthwise_6x_cast_fp16")];
+            tensor<string, []> Conv2D_8x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_8x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_8x_strides_0 = const()[name = tensor<string, []>("Conv2D_8x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_8x_dilations_0 = const()[name = tensor<string, []>("Conv2D_8x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_8x_groups_0 = const()[name = tensor<string, []>("Conv2D_8x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_8x_pad_0 = const()[name = tensor<string, []>("Conv2D_8x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [56, 48, 1, 1]> transpose_30_to_fp16 = const()[name = tensor<string, []>("transpose_30_to_fp16"), val = tensor<fp16, [56, 48, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(23744)))];
+            tensor<fp16, [56]> const_46_to_fp16 = const()[name = tensor<string, []>("const_46_to_fp16"), val = tensor<fp16, [56]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29184)))];
+            tensor<fp16, [1, 56, 16, 16]> conv2d_7_1_cast_fp16 = conv(bias = const_46_to_fp16, dilations = Conv2D_8x_dilations_0, groups = Conv2D_8x_groups_0, pad = Conv2D_8x_pad_0, pad_type = Conv2D_8x_pad_type_0, strides = Conv2D_8x_strides_0, weight = transpose_30_to_fp16, x = depthwise_6x_cast_fp16)[name = tensor<string, []>("conv2d_7_1_cast_fp16")];
+            tensor<fp16, [1, 56, 16, 16]> add_6__xeno_compat__1_cast_fp16 = add(x = pad_5_cast_fp16, y = conv2d_7_1_cast_fp16)[name = tensor<string, []>("add_6__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 56, 16, 16]> activation_7_cast_fp16 = relu(x = add_6__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_7_cast_fp16")];
+            tensor<string, []> pad_6_mode_0 = const()[name = tensor<string, []>("pad_6_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_23 = const()[name = tensor<string, []>("const_23"), val = tensor<int32, [8]>([0, 0, 0, 8, 0, 0, 0, 0])];
+            tensor<fp16, []> const_6_to_fp16 = const()[name = tensor<string, []>("const_6_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 64, 16, 16]> pad_6_cast_fp16 = pad(constant_val = const_6_to_fp16, mode = pad_6_mode_0, pad = const_23, x = activation_7_cast_fp16)[name = tensor<string, []>("pad_6_cast_fp16")];
+            tensor<string, []> depthwise_7x_pad_type_0 = const()[name = tensor<string, []>("depthwise_7x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_7x_strides_0 = const()[name = tensor<string, []>("depthwise_7x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_7x_dilations_0 = const()[name = tensor<string, []>("depthwise_7x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_7x_groups_0 = const()[name = tensor<string, []>("depthwise_7x_groups_0"), val = tensor<int32, []>(56)];
+            tensor<int32, [4]> depthwise_7x_pad_0 = const()[name = tensor<string, []>("depthwise_7x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [56, 1, 3, 3]> transpose_32_to_fp16 = const()[name = tensor<string, []>("transpose_32_to_fp16"), val = tensor<fp16, [56, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(29376)))];
+            tensor<fp16, [1, 56, 16, 16]> depthwise_7x_cast_fp16 = conv(dilations = depthwise_7x_dilations_0, groups = depthwise_7x_groups_0, pad = depthwise_7x_pad_0, pad_type = depthwise_7x_pad_type_0, strides = depthwise_7x_strides_0, weight = transpose_32_to_fp16, x = activation_7_cast_fp16)[name = tensor<string, []>("depthwise_7x_cast_fp16")];
+            tensor<string, []> Conv2D_9x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_9x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_9x_strides_0 = const()[name = tensor<string, []>("Conv2D_9x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_9x_dilations_0 = const()[name = tensor<string, []>("Conv2D_9x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_9x_groups_0 = const()[name = tensor<string, []>("Conv2D_9x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_9x_pad_0 = const()[name = tensor<string, []>("Conv2D_9x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 56, 1, 1]> transpose_34_to_fp16 = const()[name = tensor<string, []>("transpose_34_to_fp16"), val = tensor<fp16, [64, 56, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(30464)))];
+            tensor<fp16, [64]> const_47_to_fp16 = const()[name = tensor<string, []>("const_47_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37696)))];
+            tensor<fp16, [1, 64, 16, 16]> conv2d_8_1_cast_fp16 = conv(bias = const_47_to_fp16, dilations = Conv2D_9x_dilations_0, groups = Conv2D_9x_groups_0, pad = Conv2D_9x_pad_0, pad_type = Conv2D_9x_pad_type_0, strides = Conv2D_9x_strides_0, weight = transpose_34_to_fp16, x = depthwise_7x_cast_fp16)[name = tensor<string, []>("conv2d_8_1_cast_fp16")];
+            tensor<fp16, [1, 64, 16, 16]> add_7__xeno_compat__1_cast_fp16 = add(x = pad_6_cast_fp16, y = conv2d_8_1_cast_fp16)[name = tensor<string, []>("add_7__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 64, 16, 16]> activation_8_cast_fp16 = relu(x = add_7__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_8_cast_fp16")];
+            tensor<string, []> pad_7_mode_0 = const()[name = tensor<string, []>("pad_7_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_24 = const()[name = tensor<string, []>("const_24"), val = tensor<int32, [8]>([0, 0, 0, 8, 0, 0, 0, 0])];
+            tensor<fp16, []> const_7_to_fp16 = const()[name = tensor<string, []>("const_7_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 72, 16, 16]> pad_7_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = pad_7_mode_0, pad = const_24, x = activation_8_cast_fp16)[name = tensor<string, []>("pad_7_cast_fp16")];
+            tensor<string, []> depthwise_8x_pad_type_0 = const()[name = tensor<string, []>("depthwise_8x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_8x_strides_0 = const()[name = tensor<string, []>("depthwise_8x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_8x_dilations_0 = const()[name = tensor<string, []>("depthwise_8x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_8x_groups_0 = const()[name = tensor<string, []>("depthwise_8x_groups_0"), val = tensor<int32, []>(64)];
+            tensor<int32, [4]> depthwise_8x_pad_0 = const()[name = tensor<string, []>("depthwise_8x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 1, 3, 3]> transpose_36_to_fp16 = const()[name = tensor<string, []>("transpose_36_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37888)))];
+            tensor<fp16, [1, 64, 16, 16]> depthwise_8x_cast_fp16 = conv(dilations = depthwise_8x_dilations_0, groups = depthwise_8x_groups_0, pad = depthwise_8x_pad_0, pad_type = depthwise_8x_pad_type_0, strides = depthwise_8x_strides_0, weight = transpose_36_to_fp16, x = activation_8_cast_fp16)[name = tensor<string, []>("depthwise_8x_cast_fp16")];
+            tensor<string, []> Conv2D_10x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_10x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_10x_strides_0 = const()[name = tensor<string, []>("Conv2D_10x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_10x_dilations_0 = const()[name = tensor<string, []>("Conv2D_10x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_10x_groups_0 = const()[name = tensor<string, []>("Conv2D_10x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_10x_pad_0 = const()[name = tensor<string, []>("Conv2D_10x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [72, 64, 1, 1]> transpose_38_to_fp16 = const()[name = tensor<string, []>("transpose_38_to_fp16"), val = tensor<fp16, [72, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(39104)))];
+            tensor<fp16, [72]> const_48_to_fp16 = const()[name = tensor<string, []>("const_48_to_fp16"), val = tensor<fp16, [72]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48384)))];
+            tensor<fp16, [1, 72, 16, 16]> conv2d_9_1_cast_fp16 = conv(bias = const_48_to_fp16, dilations = Conv2D_10x_dilations_0, groups = Conv2D_10x_groups_0, pad = Conv2D_10x_pad_0, pad_type = Conv2D_10x_pad_type_0, strides = Conv2D_10x_strides_0, weight = transpose_38_to_fp16, x = depthwise_8x_cast_fp16)[name = tensor<string, []>("conv2d_9_1_cast_fp16")];
+            tensor<fp16, [1, 72, 16, 16]> add_8__xeno_compat__1_cast_fp16 = add(x = pad_7_cast_fp16, y = conv2d_9_1_cast_fp16)[name = tensor<string, []>("add_8__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 72, 16, 16]> activation_9_cast_fp16 = relu(x = add_8__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_9_cast_fp16")];
+            tensor<string, []> pad_8_mode_0 = const()[name = tensor<string, []>("pad_8_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_25 = const()[name = tensor<string, []>("const_25"), val = tensor<int32, [8]>([0, 0, 0, 8, 0, 0, 0, 0])];
+            tensor<fp16, []> const_8_to_fp16 = const()[name = tensor<string, []>("const_8_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 80, 16, 16]> pad_8_cast_fp16 = pad(constant_val = const_8_to_fp16, mode = pad_8_mode_0, pad = const_25, x = activation_9_cast_fp16)[name = tensor<string, []>("pad_8_cast_fp16")];
+            tensor<string, []> depthwise_9x_pad_type_0 = const()[name = tensor<string, []>("depthwise_9x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_9x_strides_0 = const()[name = tensor<string, []>("depthwise_9x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_9x_dilations_0 = const()[name = tensor<string, []>("depthwise_9x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_9x_groups_0 = const()[name = tensor<string, []>("depthwise_9x_groups_0"), val = tensor<int32, []>(72)];
+            tensor<int32, [4]> depthwise_9x_pad_0 = const()[name = tensor<string, []>("depthwise_9x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [72, 1, 3, 3]> transpose_40_to_fp16 = const()[name = tensor<string, []>("transpose_40_to_fp16"), val = tensor<fp16, [72, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(48640)))];
+            tensor<fp16, [1, 72, 16, 16]> depthwise_9x_cast_fp16 = conv(dilations = depthwise_9x_dilations_0, groups = depthwise_9x_groups_0, pad = depthwise_9x_pad_0, pad_type = depthwise_9x_pad_type_0, strides = depthwise_9x_strides_0, weight = transpose_40_to_fp16, x = activation_9_cast_fp16)[name = tensor<string, []>("depthwise_9x_cast_fp16")];
+            tensor<string, []> Conv2D_11x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_11x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_11x_strides_0 = const()[name = tensor<string, []>("Conv2D_11x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_11x_dilations_0 = const()[name = tensor<string, []>("Conv2D_11x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_11x_groups_0 = const()[name = tensor<string, []>("Conv2D_11x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_11x_pad_0 = const()[name = tensor<string, []>("Conv2D_11x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [80, 72, 1, 1]> transpose_42_to_fp16 = const()[name = tensor<string, []>("transpose_42_to_fp16"), val = tensor<fp16, [80, 72, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(50048)))];
+            tensor<fp16, [80]> const_49_to_fp16 = const()[name = tensor<string, []>("const_49_to_fp16"), val = tensor<fp16, [80]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61632)))];
+            tensor<fp16, [1, 80, 16, 16]> conv2d_10_1_cast_fp16 = conv(bias = const_49_to_fp16, dilations = Conv2D_11x_dilations_0, groups = Conv2D_11x_groups_0, pad = Conv2D_11x_pad_0, pad_type = Conv2D_11x_pad_type_0, strides = Conv2D_11x_strides_0, weight = transpose_42_to_fp16, x = depthwise_9x_cast_fp16)[name = tensor<string, []>("conv2d_10_1_cast_fp16")];
+            tensor<fp16, [1, 80, 16, 16]> add_9__xeno_compat__1_cast_fp16 = add(x = pad_8_cast_fp16, y = conv2d_10_1_cast_fp16)[name = tensor<string, []>("add_9__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 80, 16, 16]> activation_10_cast_fp16 = relu(x = add_9__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_10_cast_fp16")];
+            tensor<string, []> pad_9_mode_0 = const()[name = tensor<string, []>("pad_9_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_26 = const()[name = tensor<string, []>("const_26"), val = tensor<int32, [8]>([0, 0, 0, 8, 0, 0, 0, 0])];
+            tensor<fp16, []> const_9_to_fp16 = const()[name = tensor<string, []>("const_9_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 88, 16, 16]> pad_9_cast_fp16 = pad(constant_val = const_9_to_fp16, mode = pad_9_mode_0, pad = const_26, x = activation_10_cast_fp16)[name = tensor<string, []>("pad_9_cast_fp16")];
+            tensor<string, []> depthwise_10x_pad_type_0 = const()[name = tensor<string, []>("depthwise_10x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_10x_strides_0 = const()[name = tensor<string, []>("depthwise_10x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_10x_dilations_0 = const()[name = tensor<string, []>("depthwise_10x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_10x_groups_0 = const()[name = tensor<string, []>("depthwise_10x_groups_0"), val = tensor<int32, []>(80)];
+            tensor<int32, [4]> depthwise_10x_pad_0 = const()[name = tensor<string, []>("depthwise_10x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [80, 1, 3, 3]> transpose_44_to_fp16 = const()[name = tensor<string, []>("transpose_44_to_fp16"), val = tensor<fp16, [80, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(61888)))];
+            tensor<fp16, [1, 80, 16, 16]> depthwise_10x_cast_fp16 = conv(dilations = depthwise_10x_dilations_0, groups = depthwise_10x_groups_0, pad = depthwise_10x_pad_0, pad_type = depthwise_10x_pad_type_0, strides = depthwise_10x_strides_0, weight = transpose_44_to_fp16, x = activation_10_cast_fp16)[name = tensor<string, []>("depthwise_10x_cast_fp16")];
+            tensor<string, []> Conv2D_12x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_12x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_12x_strides_0 = const()[name = tensor<string, []>("Conv2D_12x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_12x_dilations_0 = const()[name = tensor<string, []>("Conv2D_12x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_12x_groups_0 = const()[name = tensor<string, []>("Conv2D_12x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_12x_pad_0 = const()[name = tensor<string, []>("Conv2D_12x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [88, 80, 1, 1]> transpose_46_to_fp16 = const()[name = tensor<string, []>("transpose_46_to_fp16"), val = tensor<fp16, [88, 80, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(63424)))];
+            tensor<fp16, [88]> const_50_to_fp16 = const()[name = tensor<string, []>("const_50_to_fp16"), val = tensor<fp16, [88]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77568)))];
+            tensor<fp16, [1, 88, 16, 16]> conv2d_11_1_cast_fp16 = conv(bias = const_50_to_fp16, dilations = Conv2D_12x_dilations_0, groups = Conv2D_12x_groups_0, pad = Conv2D_12x_pad_0, pad_type = Conv2D_12x_pad_type_0, strides = Conv2D_12x_strides_0, weight = transpose_46_to_fp16, x = depthwise_10x_cast_fp16)[name = tensor<string, []>("conv2d_11_1_cast_fp16")];
+            tensor<fp16, [1, 88, 16, 16]> add_10__xeno_compat__1_cast_fp16 = add(x = pad_9_cast_fp16, y = conv2d_11_1_cast_fp16)[name = tensor<string, []>("add_10__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 88, 16, 16]> activation_11_cast_fp16 = relu(x = add_10__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_11_cast_fp16")];
+            tensor<string, []> Conv2D_18x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_18x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> Conv2D_18x_strides_0 = const()[name = tensor<string, []>("Conv2D_18x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_18x_dilations_0 = const()[name = tensor<string, []>("Conv2D_18x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_18x_groups_0 = const()[name = tensor<string, []>("Conv2D_18x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_18x_pad_0 = const()[name = tensor<string, []>("Conv2D_18x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [2, 88, 1, 1]> conv_0_weight_0_to_fp16 = const()[name = tensor<string, []>("conv_0_weight_0_to_fp16"), val = tensor<fp16, [2, 88, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(77824)))];
+            tensor<fp16, [2]> conv_0_bias_0_to_fp16 = const()[name = tensor<string, []>("conv_0_bias_0_to_fp16"), val = tensor<fp16, [2]>([0x1.264p-1, 0x1.458p-3])];
+            tensor<fp16, [1, 2, 16, 16]> conv_0_cast_fp16 = conv(bias = conv_0_bias_0_to_fp16, dilations = Conv2D_18x_dilations_0, groups = Conv2D_18x_groups_0, pad = Conv2D_18x_pad_0, pad_type = Conv2D_18x_pad_type_0, strides = Conv2D_18x_strides_0, weight = conv_0_weight_0_to_fp16, x = activation_11_cast_fp16)[name = tensor<string, []>("conv_0_cast_fp16")];
+            tensor<int32, [4]> Conv2D_18_perm_0 = const()[name = tensor<string, []>("Conv2D_18_perm_0"), val = tensor<int32, [4]>([0, 2, 3, 1])];
+            tensor<int32, [2]> max_pool_2_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_2_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_2_strides_0 = const()[name = tensor<string, []>("max_pool_2_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_2_pad_type_0 = const()[name = tensor<string, []>("max_pool_2_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [4]> max_pool_2_pad_0 = const()[name = tensor<string, []>("max_pool_2_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_2_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_2_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 88, 8, 8]> max_pool_2_cast_fp16 = max_pool(ceil_mode = max_pool_2_ceil_mode_0, kernel_sizes = max_pool_2_kernel_sizes_0, pad = max_pool_2_pad_0, pad_type = max_pool_2_pad_type_0, strides = max_pool_2_strides_0, x = activation_11_cast_fp16)[name = tensor<string, []>("max_pool_2_cast_fp16")];
+            tensor<string, []> depthwise_11x_pad_type_0 = const()[name = tensor<string, []>("depthwise_11x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_11x_strides_0 = const()[name = tensor<string, []>("depthwise_11x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_11x_dilations_0 = const()[name = tensor<string, []>("depthwise_11x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_11x_groups_0 = const()[name = tensor<string, []>("depthwise_11x_groups_0"), val = tensor<int32, []>(88)];
+            tensor<int32, [4]> depthwise_11x_pad_0 = const()[name = tensor<string, []>("depthwise_11x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [88, 1, 3, 3]> transpose_51_to_fp16 = const()[name = tensor<string, []>("transpose_51_to_fp16"), val = tensor<fp16, [88, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(78272)))];
+            tensor<fp16, [1, 88, 8, 8]> depthwise_11x_cast_fp16 = conv(dilations = depthwise_11x_dilations_0, groups = depthwise_11x_groups_0, pad = depthwise_11x_pad_0, pad_type = depthwise_11x_pad_type_0, strides = depthwise_11x_strides_0, weight = transpose_51_to_fp16, x = activation_11_cast_fp16)[name = tensor<string, []>("depthwise_11x_cast_fp16")];
+            tensor<string, []> Conv2D_20x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_20x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> Conv2D_20x_strides_0 = const()[name = tensor<string, []>("Conv2D_20x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_20x_dilations_0 = const()[name = tensor<string, []>("Conv2D_20x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_20x_groups_0 = const()[name = tensor<string, []>("Conv2D_20x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_20x_pad_0 = const()[name = tensor<string, []>("Conv2D_20x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 88, 1, 1]> conv_1_weight_0_to_fp16 = const()[name = tensor<string, []>("conv_1_weight_0_to_fp16"), val = tensor<fp16, [32, 88, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(79936)))];
+            tensor<fp16, [32]> conv_1_bias_0_to_fp16 = const()[name = tensor<string, []>("conv_1_bias_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85632)))];
+            tensor<fp16, [1, 32, 16, 16]> conv_1_cast_fp16 = conv(bias = conv_1_bias_0_to_fp16, dilations = Conv2D_20x_dilations_0, groups = Conv2D_20x_groups_0, pad = Conv2D_20x_pad_0, pad_type = Conv2D_20x_pad_type_0, strides = Conv2D_20x_strides_0, weight = conv_1_weight_0_to_fp16, x = activation_11_cast_fp16)[name = tensor<string, []>("conv_1_cast_fp16")];
+            tensor<int32, [4]> Conv2D_20_perm_0 = const()[name = tensor<string, []>("Conv2D_20_perm_0"), val = tensor<int32, [4]>([0, 2, 3, 1])];
+            tensor<string, []> pad_10_mode_0 = const()[name = tensor<string, []>("pad_10_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_34 = const()[name = tensor<string, []>("const_34"), val = tensor<int32, [8]>([0, 0, 0, 8, 0, 0, 0, 0])];
+            tensor<fp16, []> const_10_to_fp16 = const()[name = tensor<string, []>("const_10_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 96, 8, 8]> pad_10_cast_fp16 = pad(constant_val = const_10_to_fp16, mode = pad_10_mode_0, pad = const_34, x = max_pool_2_cast_fp16)[name = tensor<string, []>("pad_10_cast_fp16")];
+            tensor<string, []> Conv2D_13x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_13x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_13x_strides_0 = const()[name = tensor<string, []>("Conv2D_13x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_13x_dilations_0 = const()[name = tensor<string, []>("Conv2D_13x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_13x_groups_0 = const()[name = tensor<string, []>("Conv2D_13x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_13x_pad_0 = const()[name = tensor<string, []>("Conv2D_13x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 88, 1, 1]> transpose_55_to_fp16 = const()[name = tensor<string, []>("transpose_55_to_fp16"), val = tensor<fp16, [96, 88, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(85760)))];
+            tensor<fp16, [96]> const_51_to_fp16 = const()[name = tensor<string, []>("const_51_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102720)))];
+            tensor<fp16, [1, 96, 8, 8]> conv2d_12_1_cast_fp16 = conv(bias = const_51_to_fp16, dilations = Conv2D_13x_dilations_0, groups = Conv2D_13x_groups_0, pad = Conv2D_13x_pad_0, pad_type = Conv2D_13x_pad_type_0, strides = Conv2D_13x_strides_0, weight = transpose_55_to_fp16, x = depthwise_11x_cast_fp16)[name = tensor<string, []>("conv2d_12_1_cast_fp16")];
+            tensor<fp16, [1, 16, 16, 2]> classificator_8_cast_fp16 = transpose(perm = Conv2D_18_perm_0, x = conv_0_cast_fp16)[name = tensor<string, []>("transpose_80")];
+            tensor<fp16, [1, 512, 1]> reshape_cast_fp16 = reshape(shape = reshape_shape, x = classificator_8_cast_fp16)[name = tensor<string, []>("reshape_cast_fp16")];
+            tensor<fp16, [1, 16, 16, 32]> regressor_8_cast_fp16 = transpose(perm = Conv2D_20_perm_0, x = conv_1_cast_fp16)[name = tensor<string, []>("transpose_79")];
+            tensor<fp16, [1, 512, 16]> reshape_1_1_cast_fp16 = reshape(shape = reshape_1_shape, x = regressor_8_cast_fp16)[name = tensor<string, []>("reshape_1_1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> add_11__xeno_compat__1_cast_fp16 = add(x = pad_10_cast_fp16, y = conv2d_12_1_cast_fp16)[name = tensor<string, []>("add_11__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> activation_12_cast_fp16 = relu(x = add_11__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_12_cast_fp16")];
+            tensor<string, []> depthwise_12x_pad_type_0 = const()[name = tensor<string, []>("depthwise_12x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_12x_strides_0 = const()[name = tensor<string, []>("depthwise_12x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_12x_dilations_0 = const()[name = tensor<string, []>("depthwise_12x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_12x_groups_0 = const()[name = tensor<string, []>("depthwise_12x_groups_0"), val = tensor<int32, []>(96)];
+            tensor<int32, [4]> depthwise_12x_pad_0 = const()[name = tensor<string, []>("depthwise_12x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 1, 3, 3]> transpose_57_to_fp16 = const()[name = tensor<string, []>("transpose_57_to_fp16"), val = tensor<fp16, [96, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(102976)))];
+            tensor<fp16, [1, 96, 8, 8]> depthwise_12x_cast_fp16 = conv(dilations = depthwise_12x_dilations_0, groups = depthwise_12x_groups_0, pad = depthwise_12x_pad_0, pad_type = depthwise_12x_pad_type_0, strides = depthwise_12x_strides_0, weight = transpose_57_to_fp16, x = activation_12_cast_fp16)[name = tensor<string, []>("depthwise_12x_cast_fp16")];
+            tensor<string, []> Conv2D_14x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_14x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_14x_strides_0 = const()[name = tensor<string, []>("Conv2D_14x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_14x_dilations_0 = const()[name = tensor<string, []>("Conv2D_14x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_14x_groups_0 = const()[name = tensor<string, []>("Conv2D_14x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_14x_pad_0 = const()[name = tensor<string, []>("Conv2D_14x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 96, 1, 1]> transpose_59_to_fp16 = const()[name = tensor<string, []>("transpose_59_to_fp16"), val = tensor<fp16, [96, 96, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(104768)))];
+            tensor<fp16, [96]> const_52_to_fp16 = const()[name = tensor<string, []>("const_52_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123264)))];
+            tensor<fp16, [1, 96, 8, 8]> conv2d_13_1_cast_fp16 = conv(bias = const_52_to_fp16, dilations = Conv2D_14x_dilations_0, groups = Conv2D_14x_groups_0, pad = Conv2D_14x_pad_0, pad_type = Conv2D_14x_pad_type_0, strides = Conv2D_14x_strides_0, weight = transpose_59_to_fp16, x = depthwise_12x_cast_fp16)[name = tensor<string, []>("conv2d_13_1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> add_12__xeno_compat__1_cast_fp16 = add(x = activation_12_cast_fp16, y = conv2d_13_1_cast_fp16)[name = tensor<string, []>("add_12__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> activation_13_cast_fp16 = relu(x = add_12__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_13_cast_fp16")];
+            tensor<string, []> depthwise_13x_pad_type_0 = const()[name = tensor<string, []>("depthwise_13x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_13x_strides_0 = const()[name = tensor<string, []>("depthwise_13x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_13x_dilations_0 = const()[name = tensor<string, []>("depthwise_13x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_13x_groups_0 = const()[name = tensor<string, []>("depthwise_13x_groups_0"), val = tensor<int32, []>(96)];
+            tensor<int32, [4]> depthwise_13x_pad_0 = const()[name = tensor<string, []>("depthwise_13x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 1, 3, 3]> transpose_61_to_fp16 = const()[name = tensor<string, []>("transpose_61_to_fp16"), val = tensor<fp16, [96, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(123520)))];
+            tensor<fp16, [1, 96, 8, 8]> depthwise_13x_cast_fp16 = conv(dilations = depthwise_13x_dilations_0, groups = depthwise_13x_groups_0, pad = depthwise_13x_pad_0, pad_type = depthwise_13x_pad_type_0, strides = depthwise_13x_strides_0, weight = transpose_61_to_fp16, x = activation_13_cast_fp16)[name = tensor<string, []>("depthwise_13x_cast_fp16")];
+            tensor<string, []> Conv2D_15x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_15x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_15x_strides_0 = const()[name = tensor<string, []>("Conv2D_15x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_15x_dilations_0 = const()[name = tensor<string, []>("Conv2D_15x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_15x_groups_0 = const()[name = tensor<string, []>("Conv2D_15x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_15x_pad_0 = const()[name = tensor<string, []>("Conv2D_15x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 96, 1, 1]> transpose_63_to_fp16 = const()[name = tensor<string, []>("transpose_63_to_fp16"), val = tensor<fp16, [96, 96, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125312)))];
+            tensor<fp16, [96]> const_53_to_fp16 = const()[name = tensor<string, []>("const_53_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(143808)))];
+            tensor<fp16, [1, 96, 8, 8]> conv2d_14_1_cast_fp16 = conv(bias = const_53_to_fp16, dilations = Conv2D_15x_dilations_0, groups = Conv2D_15x_groups_0, pad = Conv2D_15x_pad_0, pad_type = Conv2D_15x_pad_type_0, strides = Conv2D_15x_strides_0, weight = transpose_63_to_fp16, x = depthwise_13x_cast_fp16)[name = tensor<string, []>("conv2d_14_1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> add_13__xeno_compat__1_cast_fp16 = add(x = activation_13_cast_fp16, y = conv2d_14_1_cast_fp16)[name = tensor<string, []>("add_13__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> activation_14_cast_fp16 = relu(x = add_13__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_14_cast_fp16")];
+            tensor<string, []> depthwise_14x_pad_type_0 = const()[name = tensor<string, []>("depthwise_14x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_14x_strides_0 = const()[name = tensor<string, []>("depthwise_14x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_14x_dilations_0 = const()[name = tensor<string, []>("depthwise_14x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_14x_groups_0 = const()[name = tensor<string, []>("depthwise_14x_groups_0"), val = tensor<int32, []>(96)];
+            tensor<int32, [4]> depthwise_14x_pad_0 = const()[name = tensor<string, []>("depthwise_14x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 1, 3, 3]> transpose_65_to_fp16 = const()[name = tensor<string, []>("transpose_65_to_fp16"), val = tensor<fp16, [96, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(144064)))];
+            tensor<fp16, [1, 96, 8, 8]> depthwise_14x_cast_fp16 = conv(dilations = depthwise_14x_dilations_0, groups = depthwise_14x_groups_0, pad = depthwise_14x_pad_0, pad_type = depthwise_14x_pad_type_0, strides = depthwise_14x_strides_0, weight = transpose_65_to_fp16, x = activation_14_cast_fp16)[name = tensor<string, []>("depthwise_14x_cast_fp16")];
+            tensor<string, []> Conv2D_16x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_16x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_16x_strides_0 = const()[name = tensor<string, []>("Conv2D_16x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_16x_dilations_0 = const()[name = tensor<string, []>("Conv2D_16x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_16x_groups_0 = const()[name = tensor<string, []>("Conv2D_16x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_16x_pad_0 = const()[name = tensor<string, []>("Conv2D_16x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 96, 1, 1]> transpose_67_to_fp16 = const()[name = tensor<string, []>("transpose_67_to_fp16"), val = tensor<fp16, [96, 96, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(145856)))];
+            tensor<fp16, [96]> const_54_to_fp16 = const()[name = tensor<string, []>("const_54_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164352)))];
+            tensor<fp16, [1, 96, 8, 8]> conv2d_15_1_cast_fp16 = conv(bias = const_54_to_fp16, dilations = Conv2D_16x_dilations_0, groups = Conv2D_16x_groups_0, pad = Conv2D_16x_pad_0, pad_type = Conv2D_16x_pad_type_0, strides = Conv2D_16x_strides_0, weight = transpose_67_to_fp16, x = depthwise_14x_cast_fp16)[name = tensor<string, []>("conv2d_15_1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> add_14__xeno_compat__1_cast_fp16 = add(x = activation_14_cast_fp16, y = conv2d_15_1_cast_fp16)[name = tensor<string, []>("add_14__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> activation_15_cast_fp16 = relu(x = add_14__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_15_cast_fp16")];
+            tensor<string, []> depthwise_15x_pad_type_0 = const()[name = tensor<string, []>("depthwise_15x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_15x_strides_0 = const()[name = tensor<string, []>("depthwise_15x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_15x_dilations_0 = const()[name = tensor<string, []>("depthwise_15x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_15x_groups_0 = const()[name = tensor<string, []>("depthwise_15x_groups_0"), val = tensor<int32, []>(96)];
+            tensor<int32, [4]> depthwise_15x_pad_0 = const()[name = tensor<string, []>("depthwise_15x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 1, 3, 3]> transpose_69_to_fp16 = const()[name = tensor<string, []>("transpose_69_to_fp16"), val = tensor<fp16, [96, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164608)))];
+            tensor<fp16, [1, 96, 8, 8]> depthwise_15x_cast_fp16 = conv(dilations = depthwise_15x_dilations_0, groups = depthwise_15x_groups_0, pad = depthwise_15x_pad_0, pad_type = depthwise_15x_pad_type_0, strides = depthwise_15x_strides_0, weight = transpose_69_to_fp16, x = activation_15_cast_fp16)[name = tensor<string, []>("depthwise_15x_cast_fp16")];
+            tensor<string, []> Conv2D_17x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_17x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_17x_strides_0 = const()[name = tensor<string, []>("Conv2D_17x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_17x_dilations_0 = const()[name = tensor<string, []>("Conv2D_17x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_17x_groups_0 = const()[name = tensor<string, []>("Conv2D_17x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_17x_pad_0 = const()[name = tensor<string, []>("Conv2D_17x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 96, 1, 1]> transpose_71_to_fp16 = const()[name = tensor<string, []>("transpose_71_to_fp16"), val = tensor<fp16, [96, 96, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(166400)))];
+            tensor<fp16, [96]> const_55_to_fp16 = const()[name = tensor<string, []>("const_55_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(184896)))];
+            tensor<fp16, [1, 96, 8, 8]> conv2d_16_1_cast_fp16 = conv(bias = const_55_to_fp16, dilations = Conv2D_17x_dilations_0, groups = Conv2D_17x_groups_0, pad = Conv2D_17x_pad_0, pad_type = Conv2D_17x_pad_type_0, strides = Conv2D_17x_strides_0, weight = transpose_71_to_fp16, x = depthwise_15x_cast_fp16)[name = tensor<string, []>("conv2d_16_1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> add_15__xeno_compat__1_cast_fp16 = add(x = activation_15_cast_fp16, y = conv2d_16_1_cast_fp16)[name = tensor<string, []>("add_15__xeno_compat__1_cast_fp16")];
+            tensor<fp16, [1, 96, 8, 8]> activation_16_cast_fp16 = relu(x = add_15__xeno_compat__1_cast_fp16)[name = tensor<string, []>("activation_16_cast_fp16")];
+            tensor<string, []> Conv2D_19x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_19x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> Conv2D_19x_strides_0 = const()[name = tensor<string, []>("Conv2D_19x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_19x_dilations_0 = const()[name = tensor<string, []>("Conv2D_19x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_19x_groups_0 = const()[name = tensor<string, []>("Conv2D_19x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_19x_pad_0 = const()[name = tensor<string, []>("Conv2D_19x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [6, 96, 1, 1]> conv_2_weight_0_to_fp16 = const()[name = tensor<string, []>("conv_2_weight_0_to_fp16"), val = tensor<fp16, [6, 96, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(185152)))];
+            tensor<fp16, [6]> conv_2_bias_0_to_fp16 = const()[name = tensor<string, []>("conv_2_bias_0_to_fp16"), val = tensor<fp16, [6]>([0x1.3f8p-2, -0x1.974p-4, -0x1.33p-2, -0x1.2dcp-3, 0x1.d7p-2, 0x1.23cp+0])];
+            tensor<fp16, [1, 6, 8, 8]> conv_2_cast_fp16 = conv(bias = conv_2_bias_0_to_fp16, dilations = Conv2D_19x_dilations_0, groups = Conv2D_19x_groups_0, pad = Conv2D_19x_pad_0, pad_type = Conv2D_19x_pad_type_0, strides = Conv2D_19x_strides_0, weight = conv_2_weight_0_to_fp16, x = activation_16_cast_fp16)[name = tensor<string, []>("conv_2_cast_fp16")];
+            tensor<int32, [4]> Conv2D_19_perm_0 = const()[name = tensor<string, []>("Conv2D_19_perm_0"), val = tensor<int32, [4]>([0, 2, 3, 1])];
+            tensor<string, []> Conv2D_21x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_21x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> Conv2D_21x_strides_0 = const()[name = tensor<string, []>("Conv2D_21x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_21x_dilations_0 = const()[name = tensor<string, []>("Conv2D_21x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_21x_groups_0 = const()[name = tensor<string, []>("Conv2D_21x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_21x_pad_0 = const()[name = tensor<string, []>("Conv2D_21x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [96, 96, 1, 1]> conv_3_weight_0_to_fp16 = const()[name = tensor<string, []>("conv_3_weight_0_to_fp16"), val = tensor<fp16, [96, 96, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(186368)))];
+            tensor<fp16, [96]> conv_3_bias_0_to_fp16 = const()[name = tensor<string, []>("conv_3_bias_0_to_fp16"), val = tensor<fp16, [96]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(204864)))];
+            tensor<fp16, [1, 96, 8, 8]> conv_3_cast_fp16 = conv(bias = conv_3_bias_0_to_fp16, dilations = Conv2D_21x_dilations_0, groups = Conv2D_21x_groups_0, pad = Conv2D_21x_pad_0, pad_type = Conv2D_21x_pad_type_0, strides = Conv2D_21x_strides_0, weight = conv_3_weight_0_to_fp16, x = activation_16_cast_fp16)[name = tensor<string, []>("conv_3_cast_fp16")];
+            tensor<int32, [4]> Conv2D_21_perm_0 = const()[name = tensor<string, []>("Conv2D_21_perm_0"), val = tensor<int32, [4]>([0, 2, 3, 1])];
+            tensor<fp16, [1, 8, 8, 6]> classificator_16_cast_fp16 = transpose(perm = Conv2D_19_perm_0, x = conv_2_cast_fp16)[name = tensor<string, []>("transpose_78")];
+            tensor<fp16, [1, 384, 1]> reshape_2_1_cast_fp16 = reshape(shape = reshape_2_shape, x = classificator_16_cast_fp16)[name = tensor<string, []>("reshape_2_1_cast_fp16")];
+            tensor<fp16, [1, 8, 8, 96]> regressor_16_cast_fp16 = transpose(perm = Conv2D_21_perm_0, x = conv_3_cast_fp16)[name = tensor<string, []>("transpose_77")];
+            tensor<fp16, [1, 384, 16]> reshape_3_1_cast_fp16 = reshape(shape = reshape_3_shape, x = regressor_16_cast_fp16)[name = tensor<string, []>("reshape_3_1_cast_fp16")];
+            tensor<bool, []> classificators_interleave_0 = const()[name = tensor<string, []>("classificators_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 896, 1]> classificators_cast_fp16 = concat(axis = classificators_axis, interleave = classificators_interleave_0, values = (reshape_cast_fp16, reshape_2_1_cast_fp16))[name = tensor<string, []>("classificators_cast_fp16")];
+            tensor<string, []> classificators_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("classificators_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<bool, []> regressors_interleave_0 = const()[name = tensor<string, []>("regressors_interleave_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 896, 16]> regressors_cast_fp16 = concat(axis = regressors_axis, interleave = regressors_interleave_0, values = (reshape_1_1_cast_fp16, reshape_3_1_cast_fp16))[name = tensor<string, []>("regressors_cast_fp16")];
+            tensor<string, []> regressors_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("regressors_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp32, [1, 896, 16]> regressors = cast(dtype = regressors_cast_fp16_to_fp32_dtype_0, x = regressors_cast_fp16)[name = tensor<string, []>("cast_11")];
+            tensor<fp32, [1, 896, 1]> classificators = cast(dtype = classificators_cast_fp16_to_fp32_dtype_0, x = classificators_cast_fp16)[name = tensor<string, []>("cast_12")];
+        } -> (classificators, regressors);
+}

compiled/face_detection_short_range.mlmodelc/weights/weight.bin

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+size 205120

compiled/face_landmark.mlmodelc/analytics/coremldata.bin

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+size 243

compiled/face_landmark.mlmodelc/coremldata.bin

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+size 294

compiled/face_landmark.mlmodelc/metadata.json

@@ -0,0 +1,75 @@
+[
+  {
+    "metadataOutputVersion" : "3.0",
+    "storagePrecision" : "Float16",
+    "outputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1 × 1 × 1404)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 1, 1404]",
+        "name" : "conv2d_21_1",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1 × 1 × 1)",
+        "shortDescription" : "",
+        "shape" : "[1, 1, 1, 1]",
+        "name" : "conv2d_31",
+        "type" : "MultiArray"
+      }
+    ],
+    "modelParameters" : [
+
+    ],
+    "specificationVersion" : 6,
+    "mlProgramOperationTypeHistogram" : {
+      "Cast" : 3,
+      "Conv" : 45,
+      "Prelu" : 23,
+      "Pad" : 3,
+      "Transpose" : 3,
+      "Add" : 20,
+      "MaxPool" : 5
+    },
+    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "isUpdatable" : "0",
+    "stateSchema" : [
+
+    ],
+    "availability" : {
+      "macOS" : "12.0",
+      "tvOS" : "15.0",
+      "visionOS" : "1.0",
+      "watchOS" : "8.0",
+      "iOS" : "15.0",
+      "macCatalyst" : "15.0"
+    },
+    "modelType" : {
+      "name" : "MLModelType_mlProgram"
+    },
+    "userDefinedMetadata" : {
+      "com.github.apple.coremltools.version" : "8.3.0",
+      "com.github.apple.coremltools.source" : "tensorflow==2.16.2"
+    },
+    "inputSchema" : [
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 192 × 192 × 3)",
+        "shortDescription" : "",
+        "shape" : "[1, 192, 192, 3]",
+        "name" : "input_1",
+        "type" : "MultiArray"
+      }
+    ],
+    "generatedClassName" : "face_landmark",
+    "method" : "predict"
+  }
+]

compiled/face_landmark.mlmodelc/model.mil

@@ -0,0 +1,466 @@
+program(1.0)
+[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-tensorflow", "2.16.2"}, {"coremltools-version", "8.3.0"}})]
+{
+    func main<ios15>(tensor<fp32, [1, 192, 192, 3]> input_1) {
+            tensor<int32, [4]> transpose_1_perm_0 = const()[name = tensor<string, []>("transpose_1_perm_0"), val = tensor<int32, [4]>([0, 3, 1, 2])];
+            tensor<string, []> input_1_to_fp16_dtype_0 = const()[name = tensor<string, []>("input_1_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
+            tensor<string, []> Conv2Dx_pad_type_0 = const()[name = tensor<string, []>("Conv2Dx_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> Conv2Dx_strides_0 = const()[name = tensor<string, []>("Conv2Dx_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> Conv2Dx_dilations_0 = const()[name = tensor<string, []>("Conv2Dx_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2Dx_groups_0 = const()[name = tensor<string, []>("Conv2Dx_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2Dx_pad_0 = const()[name = tensor<string, []>("Conv2Dx_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [16, 3, 3, 3]> transpose_0_to_fp16 = const()[name = tensor<string, []>("transpose_0_to_fp16"), val = tensor<fp16, [16, 3, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
+            tensor<fp16, [16]> const_75_to_fp16 = const()[name = tensor<string, []>("const_75_to_fp16"), val = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1024)))];
+            tensor<fp16, [1, 192, 192, 3]> input_1_to_fp16 = cast(dtype = input_1_to_fp16_dtype_0, x = input_1)[name = tensor<string, []>("cast_5")];
+            tensor<fp16, [1, 3, 192, 192]> transpose_1_cast_fp16 = transpose(perm = transpose_1_perm_0, x = input_1_to_fp16)[name = tensor<string, []>("transpose_98")];
+            tensor<fp16, [1, 16, 96, 96]> conv2d_1_cast_fp16 = conv(bias = const_75_to_fp16, dilations = Conv2Dx_dilations_0, groups = Conv2Dx_groups_0, pad = Conv2Dx_pad_0, pad_type = Conv2Dx_pad_type_0, strides = Conv2Dx_strides_0, weight = transpose_0_to_fp16, x = transpose_1_cast_fp16)[name = tensor<string, []>("conv2d_1_cast_fp16")];
+            tensor<fp16, [16]> p_re_lu_1_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_1_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1152)))];
+            tensor<fp16, [1, 16, 96, 96]> p_re_lu_1_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_1_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = conv2d_1_cast_fp16)[name = tensor<string, []>("p_re_lu_1_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwisex_pad_type_0 = const()[name = tensor<string, []>("depthwisex_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwisex_strides_0 = const()[name = tensor<string, []>("depthwisex_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwisex_dilations_0 = const()[name = tensor<string, []>("depthwisex_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwisex_groups_0 = const()[name = tensor<string, []>("depthwisex_groups_0"), val = tensor<int32, []>(16)];
+            tensor<int32, [4]> depthwisex_pad_0 = const()[name = tensor<string, []>("depthwisex_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [16, 1, 3, 3]> transpose_2_to_fp16 = const()[name = tensor<string, []>("transpose_2_to_fp16"), val = tensor<fp16, [16, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1280)))];
+            tensor<fp16, [1, 16, 96, 96]> depthwisex_cast_fp16 = conv(dilations = depthwisex_dilations_0, groups = depthwisex_groups_0, pad = depthwisex_pad_0, pad_type = depthwisex_pad_type_0, strides = depthwisex_strides_0, weight = transpose_2_to_fp16, x = p_re_lu_1_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwisex_cast_fp16")];
+            tensor<string, []> Conv2D_2x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_2x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_2x_strides_0 = const()[name = tensor<string, []>("Conv2D_2x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_2x_dilations_0 = const()[name = tensor<string, []>("Conv2D_2x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_2x_groups_0 = const()[name = tensor<string, []>("Conv2D_2x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_2x_pad_0 = const()[name = tensor<string, []>("Conv2D_2x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [16, 16, 1, 1]> transpose_4_to_fp16 = const()[name = tensor<string, []>("transpose_4_to_fp16"), val = tensor<fp16, [16, 16, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1664)))];
+            tensor<fp16, [16]> const_76_to_fp16 = const()[name = tensor<string, []>("const_76_to_fp16"), val = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2240)))];
+            tensor<fp16, [1, 16, 96, 96]> conv2d_2_1_cast_fp16 = conv(bias = const_76_to_fp16, dilations = Conv2D_2x_dilations_0, groups = Conv2D_2x_groups_0, pad = Conv2D_2x_pad_0, pad_type = Conv2D_2x_pad_type_0, strides = Conv2D_2x_strides_0, weight = transpose_4_to_fp16, x = depthwisex_cast_fp16)[name = tensor<string, []>("conv2d_2_1_cast_fp16")];
+            tensor<fp16, [1, 16, 96, 96]> add_1_cast_fp16 = add(x = p_re_lu_1_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_2_1_cast_fp16)[name = tensor<string, []>("add_1_cast_fp16")];
+            tensor<fp16, [16]> p_re_lu_2_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_2_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2368)))];
+            tensor<fp16, [1, 16, 96, 96]> p_re_lu_2_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_2_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_1_cast_fp16)[name = tensor<string, []>("p_re_lu_2_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_1x_pad_type_0 = const()[name = tensor<string, []>("depthwise_1x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_1x_strides_0 = const()[name = tensor<string, []>("depthwise_1x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_1x_dilations_0 = const()[name = tensor<string, []>("depthwise_1x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_1x_groups_0 = const()[name = tensor<string, []>("depthwise_1x_groups_0"), val = tensor<int32, []>(16)];
+            tensor<int32, [4]> depthwise_1x_pad_0 = const()[name = tensor<string, []>("depthwise_1x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [16, 1, 3, 3]> transpose_6_to_fp16 = const()[name = tensor<string, []>("transpose_6_to_fp16"), val = tensor<fp16, [16, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2496)))];
+            tensor<fp16, [1, 16, 96, 96]> depthwise_1x_cast_fp16 = conv(dilations = depthwise_1x_dilations_0, groups = depthwise_1x_groups_0, pad = depthwise_1x_pad_0, pad_type = depthwise_1x_pad_type_0, strides = depthwise_1x_strides_0, weight = transpose_6_to_fp16, x = p_re_lu_2_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_1x_cast_fp16")];
+            tensor<string, []> Conv2D_3x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_3x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_3x_strides_0 = const()[name = tensor<string, []>("Conv2D_3x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_3x_dilations_0 = const()[name = tensor<string, []>("Conv2D_3x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_3x_groups_0 = const()[name = tensor<string, []>("Conv2D_3x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_3x_pad_0 = const()[name = tensor<string, []>("Conv2D_3x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [16, 16, 1, 1]> transpose_8_to_fp16 = const()[name = tensor<string, []>("transpose_8_to_fp16"), val = tensor<fp16, [16, 16, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2880)))];
+            tensor<fp16, [16]> const_77_to_fp16 = const()[name = tensor<string, []>("const_77_to_fp16"), val = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3456)))];
+            tensor<fp16, [1, 16, 96, 96]> conv2d_3_1_cast_fp16 = conv(bias = const_77_to_fp16, dilations = Conv2D_3x_dilations_0, groups = Conv2D_3x_groups_0, pad = Conv2D_3x_pad_0, pad_type = Conv2D_3x_pad_type_0, strides = Conv2D_3x_strides_0, weight = transpose_8_to_fp16, x = depthwise_1x_cast_fp16)[name = tensor<string, []>("conv2d_3_1_cast_fp16")];
+            tensor<fp16, [1, 16, 96, 96]> add_2_cast_fp16 = add(x = p_re_lu_2_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_3_1_cast_fp16)[name = tensor<string, []>("add_2_cast_fp16")];
+            tensor<fp16, [16]> p_re_lu_3_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_3_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [16]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3584)))];
+            tensor<fp16, [1, 16, 96, 96]> p_re_lu_3_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_3_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_2_cast_fp16)[name = tensor<string, []>("p_re_lu_3_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<int32, [2]> max_pool_0_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_0_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_0_strides_0 = const()[name = tensor<string, []>("max_pool_0_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_0_pad_type_0 = const()[name = tensor<string, []>("max_pool_0_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [4]> max_pool_0_pad_0 = const()[name = tensor<string, []>("max_pool_0_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_0_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_0_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 16, 48, 48]> max_pool_0_cast_fp16 = max_pool(ceil_mode = max_pool_0_ceil_mode_0, kernel_sizes = max_pool_0_kernel_sizes_0, pad = max_pool_0_pad_0, pad_type = max_pool_0_pad_type_0, strides = max_pool_0_strides_0, x = p_re_lu_3_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("max_pool_0_cast_fp16")];
+            tensor<string, []> depthwise_2x_pad_type_0 = const()[name = tensor<string, []>("depthwise_2x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_2x_strides_0 = const()[name = tensor<string, []>("depthwise_2x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_2x_dilations_0 = const()[name = tensor<string, []>("depthwise_2x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_2x_groups_0 = const()[name = tensor<string, []>("depthwise_2x_groups_0"), val = tensor<int32, []>(16)];
+            tensor<int32, [4]> depthwise_2x_pad_0 = const()[name = tensor<string, []>("depthwise_2x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [16, 1, 3, 3]> transpose_11_to_fp16 = const()[name = tensor<string, []>("transpose_11_to_fp16"), val = tensor<fp16, [16, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3712)))];
+            tensor<fp16, [1, 16, 48, 48]> depthwise_2x_cast_fp16 = conv(dilations = depthwise_2x_dilations_0, groups = depthwise_2x_groups_0, pad = depthwise_2x_pad_0, pad_type = depthwise_2x_pad_type_0, strides = depthwise_2x_strides_0, weight = transpose_11_to_fp16, x = p_re_lu_3_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_2x_cast_fp16")];
+            tensor<string, []> pad_0_mode_0 = const()[name = tensor<string, []>("pad_0_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_32 = const()[name = tensor<string, []>("const_32"), val = tensor<int32, [8]>([0, 0, 0, 16, 0, 0, 0, 0])];
+            tensor<fp16, []> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 32, 48, 48]> pad_0_cast_fp16 = pad(constant_val = const_3_to_fp16, mode = pad_0_mode_0, pad = const_32, x = max_pool_0_cast_fp16)[name = tensor<string, []>("pad_0_cast_fp16")];
+            tensor<string, []> Conv2D_4x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_4x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_4x_strides_0 = const()[name = tensor<string, []>("Conv2D_4x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_4x_dilations_0 = const()[name = tensor<string, []>("Conv2D_4x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_4x_groups_0 = const()[name = tensor<string, []>("Conv2D_4x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_4x_pad_0 = const()[name = tensor<string, []>("Conv2D_4x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 16, 1, 1]> transpose_13_to_fp16 = const()[name = tensor<string, []>("transpose_13_to_fp16"), val = tensor<fp16, [32, 16, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4096)))];
+            tensor<fp16, [32]> const_78_to_fp16 = const()[name = tensor<string, []>("const_78_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5184)))];
+            tensor<fp16, [1, 32, 48, 48]> conv2d_4_1_cast_fp16 = conv(bias = const_78_to_fp16, dilations = Conv2D_4x_dilations_0, groups = Conv2D_4x_groups_0, pad = Conv2D_4x_pad_0, pad_type = Conv2D_4x_pad_type_0, strides = Conv2D_4x_strides_0, weight = transpose_13_to_fp16, x = depthwise_2x_cast_fp16)[name = tensor<string, []>("conv2d_4_1_cast_fp16")];
+            tensor<fp16, [1, 32, 48, 48]> add_3_cast_fp16 = add(x = pad_0_cast_fp16, y = conv2d_4_1_cast_fp16)[name = tensor<string, []>("add_3_cast_fp16")];
+            tensor<fp16, [32]> p_re_lu_4_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_4_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5312)))];
+            tensor<fp16, [1, 32, 48, 48]> p_re_lu_4_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_4_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_3_cast_fp16)[name = tensor<string, []>("p_re_lu_4_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_3x_pad_type_0 = const()[name = tensor<string, []>("depthwise_3x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_3x_strides_0 = const()[name = tensor<string, []>("depthwise_3x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_3x_dilations_0 = const()[name = tensor<string, []>("depthwise_3x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_3x_groups_0 = const()[name = tensor<string, []>("depthwise_3x_groups_0"), val = tensor<int32, []>(32)];
+            tensor<int32, [4]> depthwise_3x_pad_0 = const()[name = tensor<string, []>("depthwise_3x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 1, 3, 3]> transpose_15_to_fp16 = const()[name = tensor<string, []>("transpose_15_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(5440)))];
+            tensor<fp16, [1, 32, 48, 48]> depthwise_3x_cast_fp16 = conv(dilations = depthwise_3x_dilations_0, groups = depthwise_3x_groups_0, pad = depthwise_3x_pad_0, pad_type = depthwise_3x_pad_type_0, strides = depthwise_3x_strides_0, weight = transpose_15_to_fp16, x = p_re_lu_4_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_3x_cast_fp16")];
+            tensor<string, []> Conv2D_5x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_5x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_5x_strides_0 = const()[name = tensor<string, []>("Conv2D_5x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_5x_dilations_0 = const()[name = tensor<string, []>("Conv2D_5x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_5x_groups_0 = const()[name = tensor<string, []>("Conv2D_5x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_5x_pad_0 = const()[name = tensor<string, []>("Conv2D_5x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 32, 1, 1]> transpose_17_to_fp16 = const()[name = tensor<string, []>("transpose_17_to_fp16"), val = tensor<fp16, [32, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6080)))];
+            tensor<fp16, [32]> const_79_to_fp16 = const()[name = tensor<string, []>("const_79_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8192)))];
+            tensor<fp16, [1, 32, 48, 48]> conv2d_5_1_cast_fp16 = conv(bias = const_79_to_fp16, dilations = Conv2D_5x_dilations_0, groups = Conv2D_5x_groups_0, pad = Conv2D_5x_pad_0, pad_type = Conv2D_5x_pad_type_0, strides = Conv2D_5x_strides_0, weight = transpose_17_to_fp16, x = depthwise_3x_cast_fp16)[name = tensor<string, []>("conv2d_5_1_cast_fp16")];
+            tensor<fp16, [1, 32, 48, 48]> add_4_cast_fp16 = add(x = p_re_lu_4_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_5_1_cast_fp16)[name = tensor<string, []>("add_4_cast_fp16")];
+            tensor<fp16, [32]> p_re_lu_5_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_5_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8320)))];
+            tensor<fp16, [1, 32, 48, 48]> p_re_lu_5_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_5_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_4_cast_fp16)[name = tensor<string, []>("p_re_lu_5_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_4x_pad_type_0 = const()[name = tensor<string, []>("depthwise_4x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_4x_strides_0 = const()[name = tensor<string, []>("depthwise_4x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_4x_dilations_0 = const()[name = tensor<string, []>("depthwise_4x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_4x_groups_0 = const()[name = tensor<string, []>("depthwise_4x_groups_0"), val = tensor<int32, []>(32)];
+            tensor<int32, [4]> depthwise_4x_pad_0 = const()[name = tensor<string, []>("depthwise_4x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 1, 3, 3]> transpose_19_to_fp16 = const()[name = tensor<string, []>("transpose_19_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8448)))];
+            tensor<fp16, [1, 32, 48, 48]> depthwise_4x_cast_fp16 = conv(dilations = depthwise_4x_dilations_0, groups = depthwise_4x_groups_0, pad = depthwise_4x_pad_0, pad_type = depthwise_4x_pad_type_0, strides = depthwise_4x_strides_0, weight = transpose_19_to_fp16, x = p_re_lu_5_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_4x_cast_fp16")];
+            tensor<string, []> Conv2D_6x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_6x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_6x_strides_0 = const()[name = tensor<string, []>("Conv2D_6x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_6x_dilations_0 = const()[name = tensor<string, []>("Conv2D_6x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_6x_groups_0 = const()[name = tensor<string, []>("Conv2D_6x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_6x_pad_0 = const()[name = tensor<string, []>("Conv2D_6x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 32, 1, 1]> transpose_21_to_fp16 = const()[name = tensor<string, []>("transpose_21_to_fp16"), val = tensor<fp16, [32, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9088)))];
+            tensor<fp16, [32]> const_80_to_fp16 = const()[name = tensor<string, []>("const_80_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11200)))];
+            tensor<fp16, [1, 32, 48, 48]> conv2d_6_1_cast_fp16 = conv(bias = const_80_to_fp16, dilations = Conv2D_6x_dilations_0, groups = Conv2D_6x_groups_0, pad = Conv2D_6x_pad_0, pad_type = Conv2D_6x_pad_type_0, strides = Conv2D_6x_strides_0, weight = transpose_21_to_fp16, x = depthwise_4x_cast_fp16)[name = tensor<string, []>("conv2d_6_1_cast_fp16")];
+            tensor<fp16, [1, 32, 48, 48]> add_5_cast_fp16 = add(x = p_re_lu_5_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_6_1_cast_fp16)[name = tensor<string, []>("add_5_cast_fp16")];
+            tensor<fp16, [32]> p_re_lu_6_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_6_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11328)))];
+            tensor<fp16, [1, 32, 48, 48]> p_re_lu_6_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_6_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_5_cast_fp16)[name = tensor<string, []>("p_re_lu_6_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<int32, [2]> max_pool_1_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_1_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_1_strides_0 = const()[name = tensor<string, []>("max_pool_1_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_1_pad_type_0 = const()[name = tensor<string, []>("max_pool_1_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [4]> max_pool_1_pad_0 = const()[name = tensor<string, []>("max_pool_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_1_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_1_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 32, 24, 24]> max_pool_1_cast_fp16 = max_pool(ceil_mode = max_pool_1_ceil_mode_0, kernel_sizes = max_pool_1_kernel_sizes_0, pad = max_pool_1_pad_0, pad_type = max_pool_1_pad_type_0, strides = max_pool_1_strides_0, x = p_re_lu_6_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("max_pool_1_cast_fp16")];
+            tensor<string, []> depthwise_5x_pad_type_0 = const()[name = tensor<string, []>("depthwise_5x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_5x_strides_0 = const()[name = tensor<string, []>("depthwise_5x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_5x_dilations_0 = const()[name = tensor<string, []>("depthwise_5x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_5x_groups_0 = const()[name = tensor<string, []>("depthwise_5x_groups_0"), val = tensor<int32, []>(32)];
+            tensor<int32, [4]> depthwise_5x_pad_0 = const()[name = tensor<string, []>("depthwise_5x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 1, 3, 3]> transpose_24_to_fp16 = const()[name = tensor<string, []>("transpose_24_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11456)))];
+            tensor<fp16, [1, 32, 24, 24]> depthwise_5x_cast_fp16 = conv(dilations = depthwise_5x_dilations_0, groups = depthwise_5x_groups_0, pad = depthwise_5x_pad_0, pad_type = depthwise_5x_pad_type_0, strides = depthwise_5x_strides_0, weight = transpose_24_to_fp16, x = p_re_lu_6_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_5x_cast_fp16")];
+            tensor<string, []> pad_1_mode_0 = const()[name = tensor<string, []>("pad_1_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_43 = const()[name = tensor<string, []>("const_43"), val = tensor<int32, [8]>([0, 0, 0, 32, 0, 0, 0, 0])];
+            tensor<fp16, []> const_7_to_fp16 = const()[name = tensor<string, []>("const_7_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 64, 24, 24]> pad_1_cast_fp16 = pad(constant_val = const_7_to_fp16, mode = pad_1_mode_0, pad = const_43, x = max_pool_1_cast_fp16)[name = tensor<string, []>("pad_1_cast_fp16")];
+            tensor<string, []> Conv2D_7x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_7x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_7x_strides_0 = const()[name = tensor<string, []>("Conv2D_7x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_7x_dilations_0 = const()[name = tensor<string, []>("Conv2D_7x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_7x_groups_0 = const()[name = tensor<string, []>("Conv2D_7x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_7x_pad_0 = const()[name = tensor<string, []>("Conv2D_7x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 32, 1, 1]> transpose_26_to_fp16 = const()[name = tensor<string, []>("transpose_26_to_fp16"), val = tensor<fp16, [64, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12096)))];
+            tensor<fp16, [64]> const_81_to_fp16 = const()[name = tensor<string, []>("const_81_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16256)))];
+            tensor<fp16, [1, 64, 24, 24]> conv2d_7_1_cast_fp16 = conv(bias = const_81_to_fp16, dilations = Conv2D_7x_dilations_0, groups = Conv2D_7x_groups_0, pad = Conv2D_7x_pad_0, pad_type = Conv2D_7x_pad_type_0, strides = Conv2D_7x_strides_0, weight = transpose_26_to_fp16, x = depthwise_5x_cast_fp16)[name = tensor<string, []>("conv2d_7_1_cast_fp16")];
+            tensor<fp16, [1, 64, 24, 24]> add_6_cast_fp16 = add(x = pad_1_cast_fp16, y = conv2d_7_1_cast_fp16)[name = tensor<string, []>("add_6_cast_fp16")];
+            tensor<fp16, [64]> p_re_lu_7_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_7_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16448)))];
+            tensor<fp16, [1, 64, 24, 24]> p_re_lu_7_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_7_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_6_cast_fp16)[name = tensor<string, []>("p_re_lu_7_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_6x_pad_type_0 = const()[name = tensor<string, []>("depthwise_6x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_6x_strides_0 = const()[name = tensor<string, []>("depthwise_6x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_6x_dilations_0 = const()[name = tensor<string, []>("depthwise_6x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_6x_groups_0 = const()[name = tensor<string, []>("depthwise_6x_groups_0"), val = tensor<int32, []>(64)];
+            tensor<int32, [4]> depthwise_6x_pad_0 = const()[name = tensor<string, []>("depthwise_6x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 1, 3, 3]> transpose_28_to_fp16 = const()[name = tensor<string, []>("transpose_28_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16640)))];
+            tensor<fp16, [1, 64, 24, 24]> depthwise_6x_cast_fp16 = conv(dilations = depthwise_6x_dilations_0, groups = depthwise_6x_groups_0, pad = depthwise_6x_pad_0, pad_type = depthwise_6x_pad_type_0, strides = depthwise_6x_strides_0, weight = transpose_28_to_fp16, x = p_re_lu_7_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_6x_cast_fp16")];
+            tensor<string, []> Conv2D_8x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_8x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_8x_strides_0 = const()[name = tensor<string, []>("Conv2D_8x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_8x_dilations_0 = const()[name = tensor<string, []>("Conv2D_8x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_8x_groups_0 = const()[name = tensor<string, []>("Conv2D_8x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_8x_pad_0 = const()[name = tensor<string, []>("Conv2D_8x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 64, 1, 1]> transpose_30_to_fp16 = const()[name = tensor<string, []>("transpose_30_to_fp16"), val = tensor<fp16, [64, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17856)))];
+            tensor<fp16, [64]> const_82_to_fp16 = const()[name = tensor<string, []>("const_82_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26112)))];
+            tensor<fp16, [1, 64, 24, 24]> conv2d_8_1_cast_fp16 = conv(bias = const_82_to_fp16, dilations = Conv2D_8x_dilations_0, groups = Conv2D_8x_groups_0, pad = Conv2D_8x_pad_0, pad_type = Conv2D_8x_pad_type_0, strides = Conv2D_8x_strides_0, weight = transpose_30_to_fp16, x = depthwise_6x_cast_fp16)[name = tensor<string, []>("conv2d_8_1_cast_fp16")];
+            tensor<fp16, [1, 64, 24, 24]> add_7_cast_fp16 = add(x = p_re_lu_7_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_8_1_cast_fp16)[name = tensor<string, []>("add_7_cast_fp16")];
+            tensor<fp16, [64]> p_re_lu_8_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_8_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26304)))];
+            tensor<fp16, [1, 64, 24, 24]> p_re_lu_8_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_8_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_7_cast_fp16)[name = tensor<string, []>("p_re_lu_8_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_7x_pad_type_0 = const()[name = tensor<string, []>("depthwise_7x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_7x_strides_0 = const()[name = tensor<string, []>("depthwise_7x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_7x_dilations_0 = const()[name = tensor<string, []>("depthwise_7x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_7x_groups_0 = const()[name = tensor<string, []>("depthwise_7x_groups_0"), val = tensor<int32, []>(64)];
+            tensor<int32, [4]> depthwise_7x_pad_0 = const()[name = tensor<string, []>("depthwise_7x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 1, 3, 3]> transpose_32_to_fp16 = const()[name = tensor<string, []>("transpose_32_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(26496)))];
+            tensor<fp16, [1, 64, 24, 24]> depthwise_7x_cast_fp16 = conv(dilations = depthwise_7x_dilations_0, groups = depthwise_7x_groups_0, pad = depthwise_7x_pad_0, pad_type = depthwise_7x_pad_type_0, strides = depthwise_7x_strides_0, weight = transpose_32_to_fp16, x = p_re_lu_8_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_7x_cast_fp16")];
+            tensor<string, []> Conv2D_9x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_9x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_9x_strides_0 = const()[name = tensor<string, []>("Conv2D_9x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_9x_dilations_0 = const()[name = tensor<string, []>("Conv2D_9x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_9x_groups_0 = const()[name = tensor<string, []>("Conv2D_9x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_9x_pad_0 = const()[name = tensor<string, []>("Conv2D_9x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 64, 1, 1]> transpose_34_to_fp16 = const()[name = tensor<string, []>("transpose_34_to_fp16"), val = tensor<fp16, [64, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(27712)))];
+            tensor<fp16, [64]> const_83_to_fp16 = const()[name = tensor<string, []>("const_83_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35968)))];
+            tensor<fp16, [1, 64, 24, 24]> conv2d_9_1_cast_fp16 = conv(bias = const_83_to_fp16, dilations = Conv2D_9x_dilations_0, groups = Conv2D_9x_groups_0, pad = Conv2D_9x_pad_0, pad_type = Conv2D_9x_pad_type_0, strides = Conv2D_9x_strides_0, weight = transpose_34_to_fp16, x = depthwise_7x_cast_fp16)[name = tensor<string, []>("conv2d_9_1_cast_fp16")];
+            tensor<fp16, [1, 64, 24, 24]> add_8_cast_fp16 = add(x = p_re_lu_8_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_9_1_cast_fp16)[name = tensor<string, []>("add_8_cast_fp16")];
+            tensor<fp16, [64]> p_re_lu_9_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_9_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [64]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36160)))];
+            tensor<fp16, [1, 64, 24, 24]> p_re_lu_9_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_9_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_8_cast_fp16)[name = tensor<string, []>("p_re_lu_9_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<int32, [2]> max_pool_2_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_2_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_2_strides_0 = const()[name = tensor<string, []>("max_pool_2_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_2_pad_type_0 = const()[name = tensor<string, []>("max_pool_2_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [4]> max_pool_2_pad_0 = const()[name = tensor<string, []>("max_pool_2_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_2_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_2_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 64, 12, 12]> max_pool_2_cast_fp16 = max_pool(ceil_mode = max_pool_2_ceil_mode_0, kernel_sizes = max_pool_2_kernel_sizes_0, pad = max_pool_2_pad_0, pad_type = max_pool_2_pad_type_0, strides = max_pool_2_strides_0, x = p_re_lu_9_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("max_pool_2_cast_fp16")];
+            tensor<string, []> depthwise_8x_pad_type_0 = const()[name = tensor<string, []>("depthwise_8x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_8x_strides_0 = const()[name = tensor<string, []>("depthwise_8x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_8x_dilations_0 = const()[name = tensor<string, []>("depthwise_8x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_8x_groups_0 = const()[name = tensor<string, []>("depthwise_8x_groups_0"), val = tensor<int32, []>(64)];
+            tensor<int32, [4]> depthwise_8x_pad_0 = const()[name = tensor<string, []>("depthwise_8x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [64, 1, 3, 3]> transpose_37_to_fp16 = const()[name = tensor<string, []>("transpose_37_to_fp16"), val = tensor<fp16, [64, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(36352)))];
+            tensor<fp16, [1, 64, 12, 12]> depthwise_8x_cast_fp16 = conv(dilations = depthwise_8x_dilations_0, groups = depthwise_8x_groups_0, pad = depthwise_8x_pad_0, pad_type = depthwise_8x_pad_type_0, strides = depthwise_8x_strides_0, weight = transpose_37_to_fp16, x = p_re_lu_9_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_8x_cast_fp16")];
+            tensor<string, []> pad_2_mode_0 = const()[name = tensor<string, []>("pad_2_mode_0"), val = tensor<string, []>("constant")];
+            tensor<int32, [8]> const_50 = const()[name = tensor<string, []>("const_50"), val = tensor<int32, [8]>([0, 0, 0, 64, 0, 0, 0, 0])];
+            tensor<fp16, []> const_11_to_fp16 = const()[name = tensor<string, []>("const_11_to_fp16"), val = tensor<fp16, []>(0x0p+0)];
+            tensor<fp16, [1, 128, 12, 12]> pad_2_cast_fp16 = pad(constant_val = const_11_to_fp16, mode = pad_2_mode_0, pad = const_50, x = max_pool_2_cast_fp16)[name = tensor<string, []>("pad_2_cast_fp16")];
+            tensor<string, []> Conv2D_10x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_10x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_10x_strides_0 = const()[name = tensor<string, []>("Conv2D_10x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_10x_dilations_0 = const()[name = tensor<string, []>("Conv2D_10x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_10x_groups_0 = const()[name = tensor<string, []>("Conv2D_10x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_10x_pad_0 = const()[name = tensor<string, []>("Conv2D_10x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 64, 1, 1]> transpose_39_to_fp16 = const()[name = tensor<string, []>("transpose_39_to_fp16"), val = tensor<fp16, [128, 64, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(37568)))];
+            tensor<fp16, [128]> const_84_to_fp16 = const()[name = tensor<string, []>("const_84_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54016)))];
+            tensor<fp16, [1, 128, 12, 12]> conv2d_10_1_cast_fp16 = conv(bias = const_84_to_fp16, dilations = Conv2D_10x_dilations_0, groups = Conv2D_10x_groups_0, pad = Conv2D_10x_pad_0, pad_type = Conv2D_10x_pad_type_0, strides = Conv2D_10x_strides_0, weight = transpose_39_to_fp16, x = depthwise_8x_cast_fp16)[name = tensor<string, []>("conv2d_10_1_cast_fp16")];
+            tensor<fp16, [1, 128, 12, 12]> add_9_cast_fp16 = add(x = pad_2_cast_fp16, y = conv2d_10_1_cast_fp16)[name = tensor<string, []>("add_9_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_10_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_10_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54336)))];
+            tensor<fp16, [1, 128, 12, 12]> p_re_lu_10_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_10_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_9_cast_fp16)[name = tensor<string, []>("p_re_lu_10_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_9x_pad_type_0 = const()[name = tensor<string, []>("depthwise_9x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_9x_strides_0 = const()[name = tensor<string, []>("depthwise_9x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_9x_dilations_0 = const()[name = tensor<string, []>("depthwise_9x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_9x_groups_0 = const()[name = tensor<string, []>("depthwise_9x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_9x_pad_0 = const()[name = tensor<string, []>("depthwise_9x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_41_to_fp16 = const()[name = tensor<string, []>("transpose_41_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(54656)))];
+            tensor<fp16, [1, 128, 12, 12]> depthwise_9x_cast_fp16 = conv(dilations = depthwise_9x_dilations_0, groups = depthwise_9x_groups_0, pad = depthwise_9x_pad_0, pad_type = depthwise_9x_pad_type_0, strides = depthwise_9x_strides_0, weight = transpose_41_to_fp16, x = p_re_lu_10_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_9x_cast_fp16")];
+            tensor<string, []> Conv2D_11x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_11x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_11x_strides_0 = const()[name = tensor<string, []>("Conv2D_11x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_11x_dilations_0 = const()[name = tensor<string, []>("Conv2D_11x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_11x_groups_0 = const()[name = tensor<string, []>("Conv2D_11x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_11x_pad_0 = const()[name = tensor<string, []>("Conv2D_11x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_43_to_fp16 = const()[name = tensor<string, []>("transpose_43_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(57024)))];
+            tensor<fp16, [128]> const_85_to_fp16 = const()[name = tensor<string, []>("const_85_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(89856)))];
+            tensor<fp16, [1, 128, 12, 12]> conv2d_11_1_cast_fp16 = conv(bias = const_85_to_fp16, dilations = Conv2D_11x_dilations_0, groups = Conv2D_11x_groups_0, pad = Conv2D_11x_pad_0, pad_type = Conv2D_11x_pad_type_0, strides = Conv2D_11x_strides_0, weight = transpose_43_to_fp16, x = depthwise_9x_cast_fp16)[name = tensor<string, []>("conv2d_11_1_cast_fp16")];
+            tensor<fp16, [1, 128, 12, 12]> add_10_cast_fp16 = add(x = p_re_lu_10_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_11_1_cast_fp16)[name = tensor<string, []>("add_10_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_11_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_11_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90176)))];
+            tensor<fp16, [1, 128, 12, 12]> p_re_lu_11_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_11_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_10_cast_fp16)[name = tensor<string, []>("p_re_lu_11_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_10x_pad_type_0 = const()[name = tensor<string, []>("depthwise_10x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_10x_strides_0 = const()[name = tensor<string, []>("depthwise_10x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_10x_dilations_0 = const()[name = tensor<string, []>("depthwise_10x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_10x_groups_0 = const()[name = tensor<string, []>("depthwise_10x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_10x_pad_0 = const()[name = tensor<string, []>("depthwise_10x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_45_to_fp16 = const()[name = tensor<string, []>("transpose_45_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(90496)))];
+            tensor<fp16, [1, 128, 12, 12]> depthwise_10x_cast_fp16 = conv(dilations = depthwise_10x_dilations_0, groups = depthwise_10x_groups_0, pad = depthwise_10x_pad_0, pad_type = depthwise_10x_pad_type_0, strides = depthwise_10x_strides_0, weight = transpose_45_to_fp16, x = p_re_lu_11_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_10x_cast_fp16")];
+            tensor<string, []> Conv2D_12x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_12x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_12x_strides_0 = const()[name = tensor<string, []>("Conv2D_12x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_12x_dilations_0 = const()[name = tensor<string, []>("Conv2D_12x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_12x_groups_0 = const()[name = tensor<string, []>("Conv2D_12x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_12x_pad_0 = const()[name = tensor<string, []>("Conv2D_12x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_47_to_fp16 = const()[name = tensor<string, []>("transpose_47_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(92864)))];
+            tensor<fp16, [128]> const_86_to_fp16 = const()[name = tensor<string, []>("const_86_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(125696)))];
+            tensor<fp16, [1, 128, 12, 12]> conv2d_12_1_cast_fp16 = conv(bias = const_86_to_fp16, dilations = Conv2D_12x_dilations_0, groups = Conv2D_12x_groups_0, pad = Conv2D_12x_pad_0, pad_type = Conv2D_12x_pad_type_0, strides = Conv2D_12x_strides_0, weight = transpose_47_to_fp16, x = depthwise_10x_cast_fp16)[name = tensor<string, []>("conv2d_12_1_cast_fp16")];
+            tensor<fp16, [1, 128, 12, 12]> add_11_cast_fp16 = add(x = p_re_lu_11_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_12_1_cast_fp16)[name = tensor<string, []>("add_11_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_12_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_12_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126016)))];
+            tensor<fp16, [1, 128, 12, 12]> p_re_lu_12_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_12_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_11_cast_fp16)[name = tensor<string, []>("p_re_lu_12_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<int32, [2]> max_pool_3_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_3_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_3_strides_0 = const()[name = tensor<string, []>("max_pool_3_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_3_pad_type_0 = const()[name = tensor<string, []>("max_pool_3_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [4]> max_pool_3_pad_0 = const()[name = tensor<string, []>("max_pool_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_3_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_3_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128, 6, 6]> max_pool_3_cast_fp16 = max_pool(ceil_mode = max_pool_3_ceil_mode_0, kernel_sizes = max_pool_3_kernel_sizes_0, pad = max_pool_3_pad_0, pad_type = max_pool_3_pad_type_0, strides = max_pool_3_strides_0, x = p_re_lu_12_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("max_pool_3_cast_fp16")];
+            tensor<string, []> depthwise_11x_pad_type_0 = const()[name = tensor<string, []>("depthwise_11x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_11x_strides_0 = const()[name = tensor<string, []>("depthwise_11x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_11x_dilations_0 = const()[name = tensor<string, []>("depthwise_11x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_11x_groups_0 = const()[name = tensor<string, []>("depthwise_11x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_11x_pad_0 = const()[name = tensor<string, []>("depthwise_11x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_50_to_fp16 = const()[name = tensor<string, []>("transpose_50_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(126336)))];
+            tensor<fp16, [1, 128, 6, 6]> depthwise_11x_cast_fp16 = conv(dilations = depthwise_11x_dilations_0, groups = depthwise_11x_groups_0, pad = depthwise_11x_pad_0, pad_type = depthwise_11x_pad_type_0, strides = depthwise_11x_strides_0, weight = transpose_50_to_fp16, x = p_re_lu_12_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_11x_cast_fp16")];
+            tensor<string, []> Conv2D_13x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_13x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_13x_strides_0 = const()[name = tensor<string, []>("Conv2D_13x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_13x_dilations_0 = const()[name = tensor<string, []>("Conv2D_13x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_13x_groups_0 = const()[name = tensor<string, []>("Conv2D_13x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_13x_pad_0 = const()[name = tensor<string, []>("Conv2D_13x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_52_to_fp16 = const()[name = tensor<string, []>("transpose_52_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(128704)))];
+            tensor<fp16, [128]> const_87_to_fp16 = const()[name = tensor<string, []>("const_87_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161536)))];
+            tensor<fp16, [1, 128, 6, 6]> conv2d_13_1_cast_fp16 = conv(bias = const_87_to_fp16, dilations = Conv2D_13x_dilations_0, groups = Conv2D_13x_groups_0, pad = Conv2D_13x_pad_0, pad_type = Conv2D_13x_pad_type_0, strides = Conv2D_13x_strides_0, weight = transpose_52_to_fp16, x = depthwise_11x_cast_fp16)[name = tensor<string, []>("conv2d_13_1_cast_fp16")];
+            tensor<fp16, [1, 128, 6, 6]> add_12_cast_fp16 = add(x = max_pool_3_cast_fp16, y = conv2d_13_1_cast_fp16)[name = tensor<string, []>("add_12_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_13_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_13_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(161856)))];
+            tensor<fp16, [1, 128, 6, 6]> p_re_lu_13_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_13_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_12_cast_fp16)[name = tensor<string, []>("p_re_lu_13_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_12x_pad_type_0 = const()[name = tensor<string, []>("depthwise_12x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_12x_strides_0 = const()[name = tensor<string, []>("depthwise_12x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_12x_dilations_0 = const()[name = tensor<string, []>("depthwise_12x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_12x_groups_0 = const()[name = tensor<string, []>("depthwise_12x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_12x_pad_0 = const()[name = tensor<string, []>("depthwise_12x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_54_to_fp16 = const()[name = tensor<string, []>("transpose_54_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(162176)))];
+            tensor<fp16, [1, 128, 6, 6]> depthwise_12x_cast_fp16 = conv(dilations = depthwise_12x_dilations_0, groups = depthwise_12x_groups_0, pad = depthwise_12x_pad_0, pad_type = depthwise_12x_pad_type_0, strides = depthwise_12x_strides_0, weight = transpose_54_to_fp16, x = p_re_lu_13_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_12x_cast_fp16")];
+            tensor<string, []> Conv2D_14x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_14x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_14x_strides_0 = const()[name = tensor<string, []>("Conv2D_14x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_14x_dilations_0 = const()[name = tensor<string, []>("Conv2D_14x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_14x_groups_0 = const()[name = tensor<string, []>("Conv2D_14x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_14x_pad_0 = const()[name = tensor<string, []>("Conv2D_14x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_56_to_fp16 = const()[name = tensor<string, []>("transpose_56_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(164544)))];
+            tensor<fp16, [128]> const_88_to_fp16 = const()[name = tensor<string, []>("const_88_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197376)))];
+            tensor<fp16, [1, 128, 6, 6]> conv2d_14_1_cast_fp16 = conv(bias = const_88_to_fp16, dilations = Conv2D_14x_dilations_0, groups = Conv2D_14x_groups_0, pad = Conv2D_14x_pad_0, pad_type = Conv2D_14x_pad_type_0, strides = Conv2D_14x_strides_0, weight = transpose_56_to_fp16, x = depthwise_12x_cast_fp16)[name = tensor<string, []>("conv2d_14_1_cast_fp16")];
+            tensor<fp16, [1, 128, 6, 6]> add_13_cast_fp16 = add(x = p_re_lu_13_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_14_1_cast_fp16)[name = tensor<string, []>("add_13_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_14_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_14_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(197696)))];
+            tensor<fp16, [1, 128, 6, 6]> p_re_lu_14_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_14_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_13_cast_fp16)[name = tensor<string, []>("p_re_lu_14_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_13x_pad_type_0 = const()[name = tensor<string, []>("depthwise_13x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_13x_strides_0 = const()[name = tensor<string, []>("depthwise_13x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_13x_dilations_0 = const()[name = tensor<string, []>("depthwise_13x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_13x_groups_0 = const()[name = tensor<string, []>("depthwise_13x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_13x_pad_0 = const()[name = tensor<string, []>("depthwise_13x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_58_to_fp16 = const()[name = tensor<string, []>("transpose_58_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(198016)))];
+            tensor<fp16, [1, 128, 6, 6]> depthwise_13x_cast_fp16 = conv(dilations = depthwise_13x_dilations_0, groups = depthwise_13x_groups_0, pad = depthwise_13x_pad_0, pad_type = depthwise_13x_pad_type_0, strides = depthwise_13x_strides_0, weight = transpose_58_to_fp16, x = p_re_lu_14_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_13x_cast_fp16")];
+            tensor<string, []> Conv2D_15x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_15x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_15x_strides_0 = const()[name = tensor<string, []>("Conv2D_15x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_15x_dilations_0 = const()[name = tensor<string, []>("Conv2D_15x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_15x_groups_0 = const()[name = tensor<string, []>("Conv2D_15x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_15x_pad_0 = const()[name = tensor<string, []>("Conv2D_15x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_60_to_fp16 = const()[name = tensor<string, []>("transpose_60_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(200384)))];
+            tensor<fp16, [128]> const_89_to_fp16 = const()[name = tensor<string, []>("const_89_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233216)))];
+            tensor<fp16, [1, 128, 6, 6]> conv2d_15_1_cast_fp16 = conv(bias = const_89_to_fp16, dilations = Conv2D_15x_dilations_0, groups = Conv2D_15x_groups_0, pad = Conv2D_15x_pad_0, pad_type = Conv2D_15x_pad_type_0, strides = Conv2D_15x_strides_0, weight = transpose_60_to_fp16, x = depthwise_13x_cast_fp16)[name = tensor<string, []>("conv2d_15_1_cast_fp16")];
+            tensor<fp16, [1, 128, 6, 6]> add_14_cast_fp16 = add(x = p_re_lu_14_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_15_1_cast_fp16)[name = tensor<string, []>("add_14_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_15_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_15_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233536)))];
+            tensor<fp16, [1, 128, 6, 6]> p_re_lu_15_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_15_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_14_cast_fp16)[name = tensor<string, []>("p_re_lu_15_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<int32, [2]> max_pool_4_kernel_sizes_0 = const()[name = tensor<string, []>("max_pool_4_kernel_sizes_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> max_pool_4_strides_0 = const()[name = tensor<string, []>("max_pool_4_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<string, []> max_pool_4_pad_type_0 = const()[name = tensor<string, []>("max_pool_4_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [4]> max_pool_4_pad_0 = const()[name = tensor<string, []>("max_pool_4_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<bool, []> max_pool_4_ceil_mode_0 = const()[name = tensor<string, []>("max_pool_4_ceil_mode_0"), val = tensor<bool, []>(false)];
+            tensor<fp16, [1, 128, 3, 3]> max_pool_4_cast_fp16 = max_pool(ceil_mode = max_pool_4_ceil_mode_0, kernel_sizes = max_pool_4_kernel_sizes_0, pad = max_pool_4_pad_0, pad_type = max_pool_4_pad_type_0, strides = max_pool_4_strides_0, x = p_re_lu_15_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("max_pool_4_cast_fp16")];
+            tensor<string, []> depthwise_14x_pad_type_0 = const()[name = tensor<string, []>("depthwise_14x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_14x_strides_0 = const()[name = tensor<string, []>("depthwise_14x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_14x_dilations_0 = const()[name = tensor<string, []>("depthwise_14x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_14x_groups_0 = const()[name = tensor<string, []>("depthwise_14x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_14x_pad_0 = const()[name = tensor<string, []>("depthwise_14x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_63_to_fp16 = const()[name = tensor<string, []>("transpose_63_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(233856)))];
+            tensor<fp16, [1, 128, 3, 3]> depthwise_14x_cast_fp16 = conv(dilations = depthwise_14x_dilations_0, groups = depthwise_14x_groups_0, pad = depthwise_14x_pad_0, pad_type = depthwise_14x_pad_type_0, strides = depthwise_14x_strides_0, weight = transpose_63_to_fp16, x = p_re_lu_15_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_14x_cast_fp16")];
+            tensor<string, []> depthwise_16x_pad_type_0 = const()[name = tensor<string, []>("depthwise_16x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_16x_strides_0 = const()[name = tensor<string, []>("depthwise_16x_strides_0"), val = tensor<int32, [2]>([2, 2])];
+            tensor<int32, [2]> depthwise_16x_dilations_0 = const()[name = tensor<string, []>("depthwise_16x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_16x_groups_0 = const()[name = tensor<string, []>("depthwise_16x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_16x_pad_0 = const()[name = tensor<string, []>("depthwise_16x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_66_to_fp16 = const()[name = tensor<string, []>("transpose_66_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(236224)))];
+            tensor<fp16, [1, 128, 3, 3]> depthwise_16x_cast_fp16 = conv(dilations = depthwise_16x_dilations_0, groups = depthwise_16x_groups_0, pad = depthwise_16x_pad_0, pad_type = depthwise_16x_pad_type_0, strides = depthwise_16x_strides_0, weight = transpose_66_to_fp16, x = p_re_lu_15_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_16x_cast_fp16")];
+            tensor<string, []> Conv2D_16x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_16x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_16x_strides_0 = const()[name = tensor<string, []>("Conv2D_16x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_16x_dilations_0 = const()[name = tensor<string, []>("Conv2D_16x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_16x_groups_0 = const()[name = tensor<string, []>("Conv2D_16x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_16x_pad_0 = const()[name = tensor<string, []>("Conv2D_16x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_68_to_fp16 = const()[name = tensor<string, []>("transpose_68_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(238592)))];
+            tensor<fp16, [128]> const_90_to_fp16 = const()[name = tensor<string, []>("const_90_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271424)))];
+            tensor<fp16, [1, 128, 3, 3]> conv2d_16_1_cast_fp16 = conv(bias = const_90_to_fp16, dilations = Conv2D_16x_dilations_0, groups = Conv2D_16x_groups_0, pad = Conv2D_16x_pad_0, pad_type = Conv2D_16x_pad_type_0, strides = Conv2D_16x_strides_0, weight = transpose_68_to_fp16, x = depthwise_14x_cast_fp16)[name = tensor<string, []>("conv2d_16_1_cast_fp16")];
+            tensor<string, []> Conv2D_18x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_18x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_18x_strides_0 = const()[name = tensor<string, []>("Conv2D_18x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_18x_dilations_0 = const()[name = tensor<string, []>("Conv2D_18x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_18x_groups_0 = const()[name = tensor<string, []>("Conv2D_18x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_18x_pad_0 = const()[name = tensor<string, []>("Conv2D_18x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_70_to_fp16 = const()[name = tensor<string, []>("transpose_70_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(271744)))];
+            tensor<fp16, [128]> const_91_to_fp16 = const()[name = tensor<string, []>("const_91_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304576)))];
+            tensor<fp16, [1, 128, 3, 3]> conv2d_28_cast_fp16 = conv(bias = const_91_to_fp16, dilations = Conv2D_18x_dilations_0, groups = Conv2D_18x_groups_0, pad = Conv2D_18x_pad_0, pad_type = Conv2D_18x_pad_type_0, strides = Conv2D_18x_strides_0, weight = transpose_70_to_fp16, x = depthwise_16x_cast_fp16)[name = tensor<string, []>("conv2d_28_cast_fp16")];
+            tensor<fp16, [1, 128, 3, 3]> add_15_cast_fp16 = add(x = max_pool_4_cast_fp16, y = conv2d_16_1_cast_fp16)[name = tensor<string, []>("add_15_cast_fp16")];
+            tensor<fp16, [1, 128, 3, 3]> add_23_cast_fp16 = add(x = max_pool_4_cast_fp16, y = conv2d_28_cast_fp16)[name = tensor<string, []>("add_23_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_16_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_16_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(304896)))];
+            tensor<fp16, [1, 128, 3, 3]> p_re_lu_16_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_16_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_15_cast_fp16)[name = tensor<string, []>("p_re_lu_16_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_26_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_26_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305216)))];
+            tensor<fp16, [1, 128, 3, 3]> p_re_lu_26_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_26_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_23_cast_fp16)[name = tensor<string, []>("p_re_lu_26_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_15x_pad_type_0 = const()[name = tensor<string, []>("depthwise_15x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_15x_strides_0 = const()[name = tensor<string, []>("depthwise_15x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_15x_dilations_0 = const()[name = tensor<string, []>("depthwise_15x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_15x_groups_0 = const()[name = tensor<string, []>("depthwise_15x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_15x_pad_0 = const()[name = tensor<string, []>("depthwise_15x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_72_to_fp16 = const()[name = tensor<string, []>("transpose_72_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(305536)))];
+            tensor<fp16, [1, 128, 3, 3]> depthwise_15x_cast_fp16 = conv(dilations = depthwise_15x_dilations_0, groups = depthwise_15x_groups_0, pad = depthwise_15x_pad_0, pad_type = depthwise_15x_pad_type_0, strides = depthwise_15x_strides_0, weight = transpose_72_to_fp16, x = p_re_lu_16_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_15x_cast_fp16")];
+            tensor<string, []> Conv2D_20x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_20x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_20x_strides_0 = const()[name = tensor<string, []>("Conv2D_20x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_20x_dilations_0 = const()[name = tensor<string, []>("Conv2D_20x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_20x_groups_0 = const()[name = tensor<string, []>("Conv2D_20x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_20x_pad_0 = const()[name = tensor<string, []>("Conv2D_20x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 128, 1, 1]> transpose_74_to_fp16 = const()[name = tensor<string, []>("transpose_74_to_fp16"), val = tensor<fp16, [32, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(307904)))];
+            tensor<fp16, [32]> const_92_to_fp16 = const()[name = tensor<string, []>("const_92_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316160)))];
+            tensor<fp16, [1, 32, 3, 3]> conv2d_29_cast_fp16 = conv(bias = const_92_to_fp16, dilations = Conv2D_20x_dilations_0, groups = Conv2D_20x_groups_0, pad = Conv2D_20x_pad_0, pad_type = Conv2D_20x_pad_type_0, strides = Conv2D_20x_strides_0, weight = transpose_74_to_fp16, x = p_re_lu_26_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("conv2d_29_cast_fp16")];
+            tensor<string, []> Conv2D_17x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_17x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_17x_strides_0 = const()[name = tensor<string, []>("Conv2D_17x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_17x_dilations_0 = const()[name = tensor<string, []>("Conv2D_17x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_17x_groups_0 = const()[name = tensor<string, []>("Conv2D_17x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_17x_pad_0 = const()[name = tensor<string, []>("Conv2D_17x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_76_to_fp16 = const()[name = tensor<string, []>("transpose_76_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(316288)))];
+            tensor<fp16, [128]> const_93_to_fp16 = const()[name = tensor<string, []>("const_93_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349120)))];
+            tensor<fp16, [1, 128, 3, 3]> conv2d_17_1_cast_fp16 = conv(bias = const_93_to_fp16, dilations = Conv2D_17x_dilations_0, groups = Conv2D_17x_groups_0, pad = Conv2D_17x_pad_0, pad_type = Conv2D_17x_pad_type_0, strides = Conv2D_17x_strides_0, weight = transpose_76_to_fp16, x = depthwise_15x_cast_fp16)[name = tensor<string, []>("conv2d_17_1_cast_fp16")];
+            tensor<fp16, [1, 128, 3, 3]> add_16_cast_fp16 = add(x = p_re_lu_16_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_17_1_cast_fp16)[name = tensor<string, []>("add_16_cast_fp16")];
+            tensor<fp16, [32]> p_re_lu_27_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_27_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349440)))];
+            tensor<fp16, [1, 32, 3, 3]> p_re_lu_27_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_27_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = conv2d_29_cast_fp16)[name = tensor<string, []>("p_re_lu_27_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_18x_pad_type_0 = const()[name = tensor<string, []>("depthwise_18x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_18x_strides_0 = const()[name = tensor<string, []>("depthwise_18x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_18x_dilations_0 = const()[name = tensor<string, []>("depthwise_18x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_18x_groups_0 = const()[name = tensor<string, []>("depthwise_18x_groups_0"), val = tensor<int32, []>(32)];
+            tensor<int32, [4]> depthwise_18x_pad_0 = const()[name = tensor<string, []>("depthwise_18x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 1, 3, 3]> transpose_78_to_fp16 = const()[name = tensor<string, []>("transpose_78_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(349568)))];
+            tensor<fp16, [1, 32, 3, 3]> depthwise_18x_cast_fp16 = conv(dilations = depthwise_18x_dilations_0, groups = depthwise_18x_groups_0, pad = depthwise_18x_pad_0, pad_type = depthwise_18x_pad_type_0, strides = depthwise_18x_strides_0, weight = transpose_78_to_fp16, x = p_re_lu_27_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_18x_cast_fp16")];
+            tensor<fp16, [128]> p_re_lu_17_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_17_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(350208)))];
+            tensor<fp16, [1, 128, 3, 3]> p_re_lu_17_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_17_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_16_cast_fp16)[name = tensor<string, []>("p_re_lu_17_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> Conv2D_22x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_22x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_22x_strides_0 = const()[name = tensor<string, []>("Conv2D_22x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_22x_dilations_0 = const()[name = tensor<string, []>("Conv2D_22x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_22x_groups_0 = const()[name = tensor<string, []>("Conv2D_22x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_22x_pad_0 = const()[name = tensor<string, []>("Conv2D_22x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 32, 1, 1]> transpose_80_to_fp16 = const()[name = tensor<string, []>("transpose_80_to_fp16"), val = tensor<fp16, [32, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(350528)))];
+            tensor<fp16, [32]> const_94_to_fp16 = const()[name = tensor<string, []>("const_94_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352640)))];
+            tensor<fp16, [1, 32, 3, 3]> conv2d_30_cast_fp16 = conv(bias = const_94_to_fp16, dilations = Conv2D_22x_dilations_0, groups = Conv2D_22x_groups_0, pad = Conv2D_22x_pad_0, pad_type = Conv2D_22x_pad_type_0, strides = Conv2D_22x_strides_0, weight = transpose_80_to_fp16, x = depthwise_18x_cast_fp16)[name = tensor<string, []>("conv2d_30_cast_fp16")];
+            tensor<string, []> depthwise_17x_pad_type_0 = const()[name = tensor<string, []>("depthwise_17x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_17x_strides_0 = const()[name = tensor<string, []>("depthwise_17x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_17x_dilations_0 = const()[name = tensor<string, []>("depthwise_17x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_17x_groups_0 = const()[name = tensor<string, []>("depthwise_17x_groups_0"), val = tensor<int32, []>(128)];
+            tensor<int32, [4]> depthwise_17x_pad_0 = const()[name = tensor<string, []>("depthwise_17x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 1, 3, 3]> transpose_82_to_fp16 = const()[name = tensor<string, []>("transpose_82_to_fp16"), val = tensor<fp16, [128, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(352768)))];
+            tensor<fp16, [1, 128, 3, 3]> depthwise_17x_cast_fp16 = conv(dilations = depthwise_17x_dilations_0, groups = depthwise_17x_groups_0, pad = depthwise_17x_pad_0, pad_type = depthwise_17x_pad_type_0, strides = depthwise_17x_strides_0, weight = transpose_82_to_fp16, x = p_re_lu_17_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_17x_cast_fp16")];
+            tensor<string, []> Conv2D_19x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_19x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_19x_strides_0 = const()[name = tensor<string, []>("Conv2D_19x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_19x_dilations_0 = const()[name = tensor<string, []>("Conv2D_19x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_19x_groups_0 = const()[name = tensor<string, []>("Conv2D_19x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_19x_pad_0 = const()[name = tensor<string, []>("Conv2D_19x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [128, 128, 1, 1]> transpose_84_to_fp16 = const()[name = tensor<string, []>("transpose_84_to_fp16"), val = tensor<fp16, [128, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(355136)))];
+            tensor<fp16, [128]> const_95_to_fp16 = const()[name = tensor<string, []>("const_95_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(387968)))];
+            tensor<fp16, [1, 128, 3, 3]> conv2d_18_1_cast_fp16 = conv(bias = const_95_to_fp16, dilations = Conv2D_19x_dilations_0, groups = Conv2D_19x_groups_0, pad = Conv2D_19x_pad_0, pad_type = Conv2D_19x_pad_type_0, strides = Conv2D_19x_strides_0, weight = transpose_84_to_fp16, x = depthwise_17x_cast_fp16)[name = tensor<string, []>("conv2d_18_1_cast_fp16")];
+            tensor<fp16, [1, 32, 3, 3]> add_24_cast_fp16 = add(x = p_re_lu_27_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_30_cast_fp16)[name = tensor<string, []>("add_24_cast_fp16")];
+            tensor<fp16, [1, 128, 3, 3]> add_17_cast_fp16 = add(x = p_re_lu_17_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_18_1_cast_fp16)[name = tensor<string, []>("add_17_cast_fp16")];
+            tensor<fp16, [32]> p_re_lu_28_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_28_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388288)))];
+            tensor<fp16, [1, 32, 3, 3]> p_re_lu_28_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_28_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_24_cast_fp16)[name = tensor<string, []>("p_re_lu_28_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> Conv2D_24x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_24x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_24x_strides_0 = const()[name = tensor<string, []>("Conv2D_24x_strides_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [2]> Conv2D_24x_dilations_0 = const()[name = tensor<string, []>("Conv2D_24x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_24x_groups_0 = const()[name = tensor<string, []>("Conv2D_24x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_24x_pad_0 = const()[name = tensor<string, []>("Conv2D_24x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [1, 32, 3, 3]> conv_0_weight_0_to_fp16 = const()[name = tensor<string, []>("conv_0_weight_0_to_fp16"), val = tensor<fp16, [1, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(388416)))];
+            tensor<fp16, [1]> conv_0_bias_0_to_fp16 = const()[name = tensor<string, []>("conv_0_bias_0_to_fp16"), val = tensor<fp16, [1]>([-0x1.36cp-2])];
+            tensor<fp16, [1, 1, 1, 1]> conv_0_cast_fp16 = conv(bias = conv_0_bias_0_to_fp16, dilations = Conv2D_24x_dilations_0, groups = Conv2D_24x_groups_0, pad = Conv2D_24x_pad_0, pad_type = Conv2D_24x_pad_type_0, strides = Conv2D_24x_strides_0, weight = conv_0_weight_0_to_fp16, x = p_re_lu_28_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("conv_0_cast_fp16")];
+            tensor<int32, [4]> Conv2D_24_perm_0 = const()[name = tensor<string, []>("Conv2D_24_perm_0"), val = tensor<int32, [4]>([0, 2, 3, 1])];
+            tensor<string, []> conv2d_31_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("conv2d_31_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp16, [128]> p_re_lu_18_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_18_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [128]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389056)))];
+            tensor<fp16, [1, 128, 3, 3]> p_re_lu_18_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_18_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_17_cast_fp16)[name = tensor<string, []>("p_re_lu_18_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> Conv2D_21x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_21x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_21x_strides_0 = const()[name = tensor<string, []>("Conv2D_21x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_21x_dilations_0 = const()[name = tensor<string, []>("Conv2D_21x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_21x_groups_0 = const()[name = tensor<string, []>("Conv2D_21x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_21x_pad_0 = const()[name = tensor<string, []>("Conv2D_21x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 128, 1, 1]> transpose_88_to_fp16 = const()[name = tensor<string, []>("transpose_88_to_fp16"), val = tensor<fp16, [32, 128, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(389376)))];
+            tensor<fp16, [32]> const_96_to_fp16 = const()[name = tensor<string, []>("const_96_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397632)))];
+            tensor<fp16, [1, 32, 3, 3]> conv2d_19_1_cast_fp16 = conv(bias = const_96_to_fp16, dilations = Conv2D_21x_dilations_0, groups = Conv2D_21x_groups_0, pad = Conv2D_21x_pad_0, pad_type = Conv2D_21x_pad_type_0, strides = Conv2D_21x_strides_0, weight = transpose_88_to_fp16, x = p_re_lu_18_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("conv2d_19_1_cast_fp16")];
+            tensor<fp16, [32]> p_re_lu_19_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_19_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397760)))];
+            tensor<fp16, [1, 32, 3, 3]> p_re_lu_19_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_19_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = conv2d_19_1_cast_fp16)[name = tensor<string, []>("p_re_lu_19_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> depthwise_19x_pad_type_0 = const()[name = tensor<string, []>("depthwise_19x_pad_type_0"), val = tensor<string, []>("same")];
+            tensor<int32, [2]> depthwise_19x_strides_0 = const()[name = tensor<string, []>("depthwise_19x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> depthwise_19x_dilations_0 = const()[name = tensor<string, []>("depthwise_19x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> depthwise_19x_groups_0 = const()[name = tensor<string, []>("depthwise_19x_groups_0"), val = tensor<int32, []>(32)];
+            tensor<int32, [4]> depthwise_19x_pad_0 = const()[name = tensor<string, []>("depthwise_19x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 1, 3, 3]> transpose_90_to_fp16 = const()[name = tensor<string, []>("transpose_90_to_fp16"), val = tensor<fp16, [32, 1, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(397888)))];
+            tensor<fp16, [1, 32, 3, 3]> depthwise_19x_cast_fp16 = conv(dilations = depthwise_19x_dilations_0, groups = depthwise_19x_groups_0, pad = depthwise_19x_pad_0, pad_type = depthwise_19x_pad_type_0, strides = depthwise_19x_strides_0, weight = transpose_90_to_fp16, x = p_re_lu_19_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("depthwise_19x_cast_fp16")];
+            tensor<string, []> Conv2D_23x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_23x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_23x_strides_0 = const()[name = tensor<string, []>("Conv2D_23x_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [2]> Conv2D_23x_dilations_0 = const()[name = tensor<string, []>("Conv2D_23x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_23x_groups_0 = const()[name = tensor<string, []>("Conv2D_23x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_23x_pad_0 = const()[name = tensor<string, []>("Conv2D_23x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [32, 32, 1, 1]> transpose_92_to_fp16 = const()[name = tensor<string, []>("transpose_92_to_fp16"), val = tensor<fp16, [32, 32, 1, 1]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(398528)))];
+            tensor<fp16, [32]> const_97_to_fp16 = const()[name = tensor<string, []>("const_97_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(400640)))];
+            tensor<fp16, [1, 32, 3, 3]> conv2d_20_1_cast_fp16 = conv(bias = const_97_to_fp16, dilations = Conv2D_23x_dilations_0, groups = Conv2D_23x_groups_0, pad = Conv2D_23x_pad_0, pad_type = Conv2D_23x_pad_type_0, strides = Conv2D_23x_strides_0, weight = transpose_92_to_fp16, x = depthwise_19x_cast_fp16)[name = tensor<string, []>("conv2d_20_1_cast_fp16")];
+            tensor<fp16, [1, 32, 3, 3]> add_18_cast_fp16 = add(x = p_re_lu_19_Alpha_dequantize_prelu_1_add_cast_fp16, y = conv2d_20_1_cast_fp16)[name = tensor<string, []>("add_18_cast_fp16")];
+            tensor<fp16, [32]> p_re_lu_20_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16 = const()[name = tensor<string, []>("p_re_lu_20_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(400768)))];
+            tensor<fp16, [1, 32, 3, 3]> p_re_lu_20_Alpha_dequantize_prelu_1_add_cast_fp16 = prelu(alpha = p_re_lu_20_Alpha_dequantize_prelu_1_add_alpha_0_to_fp16, x = add_18_cast_fp16)[name = tensor<string, []>("p_re_lu_20_Alpha_dequantize_prelu_1_add_cast_fp16")];
+            tensor<string, []> Conv2D_25x_pad_type_0 = const()[name = tensor<string, []>("Conv2D_25x_pad_type_0"), val = tensor<string, []>("valid")];
+            tensor<int32, [2]> Conv2D_25x_strides_0 = const()[name = tensor<string, []>("Conv2D_25x_strides_0"), val = tensor<int32, [2]>([3, 3])];
+            tensor<int32, [2]> Conv2D_25x_dilations_0 = const()[name = tensor<string, []>("Conv2D_25x_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, []> Conv2D_25x_groups_0 = const()[name = tensor<string, []>("Conv2D_25x_groups_0"), val = tensor<int32, []>(1)];
+            tensor<int32, [4]> Conv2D_25x_pad_0 = const()[name = tensor<string, []>("Conv2D_25x_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<fp16, [1404, 32, 3, 3]> conv_1_weight_0_to_fp16 = const()[name = tensor<string, []>("conv_1_weight_0_to_fp16"), val = tensor<fp16, [1404, 32, 3, 3]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(400896)))];
+            tensor<fp16, [1404]> conv_1_bias_0_to_fp16 = const()[name = tensor<string, []>("conv_1_bias_0_to_fp16"), val = tensor<fp16, [1404]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1209664)))];
+            tensor<fp16, [1, 1404, 1, 1]> conv_1_cast_fp16 = conv(bias = conv_1_bias_0_to_fp16, dilations = Conv2D_25x_dilations_0, groups = Conv2D_25x_groups_0, pad = Conv2D_25x_pad_0, pad_type = Conv2D_25x_pad_type_0, strides = Conv2D_25x_strides_0, weight = conv_1_weight_0_to_fp16, x = p_re_lu_20_Alpha_dequantize_prelu_1_add_cast_fp16)[name = tensor<string, []>("conv_1_cast_fp16")];
+            tensor<int32, [4]> Conv2D_25_perm_0 = const()[name = tensor<string, []>("Conv2D_25_perm_0"), val = tensor<int32, [4]>([0, 2, 3, 1])];
+            tensor<string, []> conv2d_21_1_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("conv2d_21_1_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
+            tensor<fp16, [1, 1, 1, 1404]> conv2d_21_1_cast_fp16 = transpose(perm = Conv2D_25_perm_0, x = conv_1_cast_fp16)[name = tensor<string, []>("transpose_96")];
+            tensor<fp32, [1, 1, 1, 1404]> conv2d_21_1 = cast(dtype = conv2d_21_1_cast_fp16_to_fp32_dtype_0, x = conv2d_21_1_cast_fp16)[name = tensor<string, []>("cast_3")];
+            tensor<fp16, [1, 1, 1, 1]> conv2d_31_cast_fp16 = transpose(perm = Conv2D_24_perm_0, x = conv_0_cast_fp16)[name = tensor<string, []>("transpose_97")];
+            tensor<fp32, [1, 1, 1, 1]> conv2d_31 = cast(dtype = conv2d_31_cast_fp16_to_fp32_dtype_0, x = conv2d_31_cast_fp16)[name = tensor<string, []>("cast_4")];
+        } -> (conv2d_21_1, conv2d_31);
+}

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