Merge "Fix hanging issue in ROI_ALIGN." into qt-dev

commit: dbd612b13b93039b106a414c74473c621d68d8fc [log] [tgz]
author: Xusong Wang <xusongw@google.com> Thu Apr 18 21:58:17 2019 +0000
committer: Android (Google) Code Review <android-gerrit@google.com> Thu Apr 18 21:58:17 2019 +0000
tree: aeb34fc403850cdf2902b5a636550a1b4ba64dc8
parent: f21abd681dae072080d8b29e48c8c68f13d1f2d1 [diff]
parent: 68ff21cdc5ffd4a9d0333a45744623f2456e0480 [diff]
diff --git a/nn/common/operations/RoiAlign.cpp b/nn/common/operations/RoiAlign.cpp
index 6b4a59d..b86caad 100644
--- a/nn/common/operations/RoiAlign.cpp
+++ b/nn/common/operations/RoiAlign.cpp

@@ -122,8 +122,11 @@
                 for (uint32_t k = 0; k < inDepth; k++) outPtr[k] = 0;
 
                 // calculate the sum of the sampling points
-                for (T_Roi y = hStart + hBinSize / 2; y < hEnd; y += hBinSize) {
-                    for (T_Roi x = wStart + wBinSize / 2; x < wEnd; x += wBinSize) {
+                for (uint32_t yInd = 0; yInd < hSamplingRatio; yInd++) {
+                    for (uint32_t xInd = 0; xInd < wSamplingRatio; xInd++) {
+                        T_Roi y = hStart + hBinSize / 2 + hBinSize * yInd;
+                        T_Roi x = wStart + wBinSize / 2 + wBinSize * xInd;
+
                         // bilinear interpolation of point (x,y)
                         // w.r.t box [(x1,y1), (x1,y2), (x2,y1), (x2,y2)]
                         uint32_t x1 = std::floor(static_cast<float>(x));
@@ -247,8 +250,11 @@
 
                 std::vector<int32_t> outTemp(inDepth, 0);
                 // calculate the sum of the sampling points
-                for (float y = hStart + hBinSize / 2; y < hEnd; y += hBinSize) {
-                    for (float x = wStart + wBinSize / 2; x < wEnd; x += wBinSize) {
+                for (uint32_t yInd = 0; yInd < hSamplingRatio; yInd++) {
+                    for (uint32_t xInd = 0; xInd < wSamplingRatio; xInd++) {
+                        float y = hStart + hBinSize / 2 + hBinSize * yInd;
+                        float x = wStart + wBinSize / 2 + wBinSize * xInd;
+
                         // bilinear interpolation of point (x,y)
                         // w.r.t box [(x1,y1), (x1,y2), (x2,y1), (x2,y2)]
                         uint32_t x1 = std::floor(x), y1 = std::floor(y);

diff --git a/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp b/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp
index 54cffd2..600be52 100644
--- a/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp
+++ b/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp

@@ -65145,6 +65145,262 @@
 
 
 #endif
+TEST_F(NeuralnetworksHidlTest, roi_align_nhwc_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nhwc_5,
+                           roi_align::is_ignored_nhwc_5,
+                           roi_align::get_examples_nhwc_5());
+}
+
+TEST_F(ValidationTest, roi_align_nhwc_5) {
+  const Model model = roi_align::createTestModel_nhwc_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nhwc_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+TEST_F(NeuralnetworksHidlTest, roi_align_nhwc_relaxed_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nhwc_relaxed_5,
+                           roi_align::is_ignored_nhwc_relaxed_5,
+                           roi_align::get_examples_nhwc_relaxed_5());
+}
+
+TEST_F(ValidationTest, roi_align_nhwc_relaxed_5) {
+  const Model model = roi_align::createTestModel_nhwc_relaxed_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nhwc_relaxed_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+TEST_F(NeuralnetworksHidlTest, roi_align_nhwc_quant8_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nhwc_quant8_5,
+                           roi_align::is_ignored_nhwc_quant8_5,
+                           roi_align::get_examples_nhwc_quant8_5());
+}
+
+TEST_F(ValidationTest, roi_align_nhwc_quant8_5) {
+  const Model model = roi_align::createTestModel_nhwc_quant8_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nhwc_quant8_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+TEST_F(NeuralnetworksHidlTest, roi_align_nhwc_float16_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nhwc_float16_5,
+                           roi_align::is_ignored_nhwc_float16_5,
+                           roi_align::get_examples_nhwc_float16_5());
+}
+
+TEST_F(ValidationTest, roi_align_nhwc_float16_5) {
+  const Model model = roi_align::createTestModel_nhwc_float16_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nhwc_float16_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+TEST_F(NeuralnetworksHidlTest, roi_align_nchw_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nchw_5,
+                           roi_align::is_ignored_nchw_5,
+                           roi_align::get_examples_nchw_5());
+}
+
+TEST_F(ValidationTest, roi_align_nchw_5) {
+  const Model model = roi_align::createTestModel_nchw_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nchw_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+TEST_F(NeuralnetworksHidlTest, roi_align_nchw_relaxed_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nchw_relaxed_5,
+                           roi_align::is_ignored_nchw_relaxed_5,
+                           roi_align::get_examples_nchw_relaxed_5());
+}
+
+TEST_F(ValidationTest, roi_align_nchw_relaxed_5) {
+  const Model model = roi_align::createTestModel_nchw_relaxed_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nchw_relaxed_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+TEST_F(NeuralnetworksHidlTest, roi_align_nchw_quant8_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nchw_quant8_5,
+                           roi_align::is_ignored_nchw_quant8_5,
+                           roi_align::get_examples_nchw_quant8_5());
+}
+
+TEST_F(ValidationTest, roi_align_nchw_quant8_5) {
+  const Model model = roi_align::createTestModel_nchw_quant8_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nchw_quant8_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+TEST_F(NeuralnetworksHidlTest, roi_align_nchw_float16_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_nchw_float16_5,
+                           roi_align::is_ignored_nchw_float16_5,
+                           roi_align::get_examples_nchw_float16_5());
+}
+
+TEST_F(ValidationTest, roi_align_nchw_float16_5) {
+  const Model model = roi_align::createTestModel_nchw_float16_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_nchw_float16_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nhwc_5,
+                           roi_align::is_ignored_dynamic_output_shape_nhwc_5,
+                           roi_align::get_examples_dynamic_output_shape_nhwc_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nhwc_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nhwc_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nhwc_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_relaxed_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nhwc_relaxed_5,
+                           roi_align::is_ignored_dynamic_output_shape_nhwc_relaxed_5,
+                           roi_align::get_examples_dynamic_output_shape_nhwc_relaxed_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nhwc_relaxed_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nhwc_relaxed_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nhwc_relaxed_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_quant8_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nhwc_quant8_5,
+                           roi_align::is_ignored_dynamic_output_shape_nhwc_quant8_5,
+                           roi_align::get_examples_dynamic_output_shape_nhwc_quant8_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nhwc_quant8_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nhwc_quant8_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nhwc_quant8_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_float16_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nhwc_float16_5,
+                           roi_align::is_ignored_dynamic_output_shape_nhwc_float16_5,
+                           roi_align::get_examples_dynamic_output_shape_nhwc_float16_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nhwc_float16_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nhwc_float16_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nhwc_float16_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nchw_5,
+                           roi_align::is_ignored_dynamic_output_shape_nchw_5,
+                           roi_align::get_examples_dynamic_output_shape_nchw_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nchw_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nchw_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nchw_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_relaxed_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nchw_relaxed_5,
+                           roi_align::is_ignored_dynamic_output_shape_nchw_relaxed_5,
+                           roi_align::get_examples_dynamic_output_shape_nchw_relaxed_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nchw_relaxed_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nchw_relaxed_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nchw_relaxed_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_quant8_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nchw_quant8_5,
+                           roi_align::is_ignored_dynamic_output_shape_nchw_quant8_5,
+                           roi_align::get_examples_dynamic_output_shape_nchw_quant8_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nchw_quant8_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nchw_quant8_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nchw_quant8_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_float16_5) {
+  generated_tests::Execute(device,
+                           roi_align::createTestModel_dynamic_output_shape_nchw_float16_5,
+                           roi_align::is_ignored_dynamic_output_shape_nchw_float16_5,
+                           roi_align::get_examples_dynamic_output_shape_nchw_float16_5(), true);
+}
+
+TEST_F(ValidationTest, roi_align_dynamic_output_shape_nchw_float16_5) {
+  const Model model = roi_align::createTestModel_dynamic_output_shape_nchw_float16_5();
+  const std::vector<Request> requests = createRequests(roi_align::get_examples_dynamic_output_shape_nchw_float16_5());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
 // Generated from: roi_pooling.mod.py.
 namespace roi_pooling {
 // Generated roi_pooling test

diff --git a/nn/runtime/test/generated/examples/roi_align.example.cpp b/nn/runtime/test/generated/examples/roi_align.example.cpp
index 025bf98..bd1d2d1 100644
--- a/nn/runtime/test/generated/examples/roi_align.example.cpp
+++ b/nn/runtime/test/generated/examples/roi_align.example.cpp

@@ -4560,3 +4560,915 @@
 return examples_zero_sized_dynamic_output_shape_nchw_float16;
 };
 
+std::vector<MixedTypedExample>& get_examples_nhwc_5() {
+static std::vector<MixedTypedExample> examples_nhwc_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nhwc_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_nhwc_relaxed_5() {
+static std::vector<MixedTypedExample> examples_nhwc_relaxed_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nhwc_relaxed_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_nhwc_quant8_5() {
+static std::vector<MixedTypedExample> examples_nhwc_quant8_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 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128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {{1, {3600, 4000, 3728, 4064}}},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nhwc_quant8_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_nhwc_float16_5() {
+static std::vector<MixedTypedExample> examples_nhwc_float16_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nhwc_float16_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_nchw_5() {
+static std::vector<MixedTypedExample> examples_nchw_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nchw_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_nchw_relaxed_5() {
+static std::vector<MixedTypedExample> examples_nchw_relaxed_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nchw_relaxed_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_nchw_quant8_5() {
+static std::vector<MixedTypedExample> examples_nchw_quant8_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 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128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 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128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {{1, {3600, 4000, 3728, 4064}}},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nchw_quant8_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_nchw_float16_5() {
+static std::vector<MixedTypedExample> examples_nchw_float16_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_nchw_float16_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nhwc_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nhwc_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nhwc_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nhwc_relaxed_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nhwc_relaxed_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nhwc_relaxed_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nhwc_quant8_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nhwc_quant8_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 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128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {{1, {3600, 4000, 3728, 4064}}},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nhwc_quant8_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nhwc_float16_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nhwc_float16_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 512, 8, 1}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 128, 4, 1}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nhwc_float16_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nchw_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nchw_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nchw_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nchw_relaxed_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nchw_relaxed_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nchw_relaxed_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nchw_quant8_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nchw_quant8_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 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128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {{1, {3600, 4000, 3728, 4064}}},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {{0, {128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128}}},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nchw_quant8_5;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape_nchw_float16_5() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape_nchw_float16_5 = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 512, 8}}, {1, {1, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 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0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {1, {450.0f, 500.0f, 466.0f, 508.0f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {1, 1, 128, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape_nchw_float16_5;
+};
+

diff --git a/nn/runtime/test/generated/models/roi_align.model.cpp b/nn/runtime/test/generated/models/roi_align.model.cpp
index b16a3f7..f6b20a1 100644
--- a/nn/runtime/test/generated/models/roi_align.model.cpp
+++ b/nn/runtime/test/generated/models/roi_align.model.cpp

@@ -104,15 +104,15 @@
 
 void CreateModel_nhwc_quant8(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 0.25f, 128);
-  OperandType type23(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 1}, 0.0625f, 128);
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type25(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 0.25f, 128);
+  OperandType type26(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 1}, 0.0625f, 128);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type22);
-  auto roi = model->addOperand(&type24);
+  auto in = model->addOperand(&type25);
+  auto roi = model->addOperand(&type27);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
@@ -121,7 +121,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type23);
+  auto out = model->addOperand(&type26);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -154,24 +154,24 @@
 
 void CreateModel_nhwc_float16(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type25(Type::TENSOR_FLOAT16, {1, 4, 4, 1});
-  OperandType type26(Type::TENSOR_FLOAT16, {4, 2, 2, 1});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
+  OperandType type28(Type::TENSOR_FLOAT16, {1, 4, 4, 1});
+  OperandType type29(Type::TENSOR_FLOAT16, {4, 2, 2, 1});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type25);
-  auto roi = model->addOperand(&type28);
+  auto in = model->addOperand(&type28);
+  auto roi = model->addOperand(&type31);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
-  auto param3 = model->addOperand(&type27);
-  auto param4 = model->addOperand(&type27);
+  auto param3 = model->addOperand(&type30);
+  auto param4 = model->addOperand(&type30);
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type26);
+  auto out = model->addOperand(&type29);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -205,13 +205,13 @@
 void CreateModel_nchw(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type29(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
-  OperandType type30(Type::TENSOR_FLOAT32, {4, 1, 2, 2});
+  OperandType type32(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
+  OperandType type33(Type::TENSOR_FLOAT32, {4, 1, 2, 2});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type29);
+  auto in = model->addOperand(&type32);
   auto roi = model->addOperand(&type2);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
@@ -221,7 +221,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type30);
+  auto out = model->addOperand(&type33);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -255,13 +255,13 @@
 void CreateModel_nchw_relaxed(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type29(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
-  OperandType type30(Type::TENSOR_FLOAT32, {4, 1, 2, 2});
+  OperandType type32(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
+  OperandType type33(Type::TENSOR_FLOAT32, {4, 1, 2, 2});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type29);
+  auto in = model->addOperand(&type32);
   auto roi = model->addOperand(&type2);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
@@ -271,7 +271,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type30);
+  auto out = model->addOperand(&type33);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -306,15 +306,15 @@
 
 void CreateModel_nchw_quant8(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
-  OperandType type31(Type::TENSOR_QUANT8_ASYMM, {1, 1, 4, 4}, 0.25f, 128);
-  OperandType type32(Type::TENSOR_QUANT8_ASYMM, {4, 1, 2, 2}, 0.0625f, 128);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type34(Type::TENSOR_QUANT8_ASYMM, {1, 1, 4, 4}, 0.25f, 128);
+  OperandType type35(Type::TENSOR_QUANT8_ASYMM, {4, 1, 2, 2}, 0.0625f, 128);
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type31);
-  auto roi = model->addOperand(&type24);
+  auto in = model->addOperand(&type34);
+  auto roi = model->addOperand(&type27);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
@@ -323,7 +323,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type32);
+  auto out = model->addOperand(&type35);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -356,24 +356,24 @@
 
 void CreateModel_nchw_float16(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
-  OperandType type33(Type::TENSOR_FLOAT16, {1, 1, 4, 4});
-  OperandType type34(Type::TENSOR_FLOAT16, {4, 1, 2, 2});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
+  OperandType type36(Type::TENSOR_FLOAT16, {1, 1, 4, 4});
+  OperandType type37(Type::TENSOR_FLOAT16, {4, 1, 2, 2});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type33);
-  auto roi = model->addOperand(&type28);
+  auto in = model->addOperand(&type36);
+  auto roi = model->addOperand(&type31);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
-  auto param3 = model->addOperand(&type27);
-  auto param4 = model->addOperand(&type27);
+  auto param3 = model->addOperand(&type30);
+  auto param4 = model->addOperand(&type30);
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type34);
+  auto out = model->addOperand(&type37);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -408,7 +408,7 @@
   OperandType type0(Type::BOOL, {});
   OperandType type1(Type::TENSOR_FLOAT32, {1, 4, 4, 1});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
@@ -423,7 +423,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type35);
+  auto out = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -458,7 +458,7 @@
   OperandType type0(Type::BOOL, {});
   OperandType type1(Type::TENSOR_FLOAT32, {1, 4, 4, 1});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
@@ -473,7 +473,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type35);
+  auto out = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -508,15 +508,15 @@
 
 void CreateModel_dynamic_output_shape_nhwc_quant8(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type22(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 0.25f, 128);
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
-  OperandType type36(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
+  OperandType type25(Type::TENSOR_QUANT8_ASYMM, {1, 4, 4, 1}, 0.25f, 128);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type22);
-  auto roi = model->addOperand(&type24);
+  auto in = model->addOperand(&type25);
+  auto roi = model->addOperand(&type27);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
@@ -525,7 +525,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type36);
+  auto out = model->addOperand(&type39);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -558,24 +558,24 @@
 
 void CreateModel_dynamic_output_shape_nhwc_float16(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type25(Type::TENSOR_FLOAT16, {1, 4, 4, 1});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type28(Type::TENSOR_FLOAT16, {1, 4, 4, 1});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type25);
-  auto roi = model->addOperand(&type28);
+  auto in = model->addOperand(&type28);
+  auto roi = model->addOperand(&type31);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
-  auto param3 = model->addOperand(&type27);
-  auto param4 = model->addOperand(&type27);
+  auto param3 = model->addOperand(&type30);
+  auto param4 = model->addOperand(&type30);
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type37);
+  auto out = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -609,13 +609,13 @@
 void CreateModel_dynamic_output_shape_nchw(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type29(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type32(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type29);
+  auto in = model->addOperand(&type32);
   auto roi = model->addOperand(&type2);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
@@ -625,7 +625,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type35);
+  auto out = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -659,13 +659,13 @@
 void CreateModel_dynamic_output_shape_nchw_relaxed(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type29(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type32(Type::TENSOR_FLOAT32, {1, 1, 4, 4});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type29);
+  auto in = model->addOperand(&type32);
   auto roi = model->addOperand(&type2);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
@@ -675,7 +675,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type35);
+  auto out = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -710,15 +710,15 @@
 
 void CreateModel_dynamic_output_shape_nchw_quant8(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
-  OperandType type31(Type::TENSOR_QUANT8_ASYMM, {1, 1, 4, 4}, 0.25f, 128);
-  OperandType type36(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type34(Type::TENSOR_QUANT8_ASYMM, {1, 1, 4, 4}, 0.25f, 128);
+  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type31);
-  auto roi = model->addOperand(&type24);
+  auto in = model->addOperand(&type34);
+  auto roi = model->addOperand(&type27);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
@@ -727,7 +727,7 @@
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type36);
+  auto out = model->addOperand(&type39);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -760,24 +760,24 @@
 
 void CreateModel_dynamic_output_shape_nchw_float16(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
-  OperandType type33(Type::TENSOR_FLOAT16, {1, 1, 4, 4});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
+  OperandType type36(Type::TENSOR_FLOAT16, {1, 1, 4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   // Phase 1, operands
-  auto in = model->addOperand(&type33);
-  auto roi = model->addOperand(&type28);
+  auto in = model->addOperand(&type36);
+  auto roi = model->addOperand(&type31);
   auto param = model->addOperand(&type4);
   auto param1 = model->addOperand(&type5);
   auto param2 = model->addOperand(&type5);
-  auto param3 = model->addOperand(&type27);
-  auto param4 = model->addOperand(&type27);
+  auto param3 = model->addOperand(&type30);
+  auto param4 = model->addOperand(&type30);
   auto param5 = model->addOperand(&type5);
   auto param6 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out = model->addOperand(&type37);
+  auto out = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param_init[] = {0, 0, 0, 0};
   model->setOperandValue(param, param_init, sizeof(int32_t) * 4);
@@ -912,15 +912,15 @@
 
 void CreateModel_nhwc_quant8_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
-  OperandType type38(Type::TENSOR_QUANT8_ASYMM, {4, 4, 8, 2}, 0.04f, 0);
-  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {4, 2, 3, 2}, 0.03125f, 10);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
+  OperandType type41(Type::TENSOR_QUANT8_ASYMM, {4, 4, 8, 2}, 0.04f, 0);
+  OperandType type42(Type::TENSOR_QUANT8_ASYMM, {4, 2, 3, 2}, 0.03125f, 10);
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type38);
-  auto roi1 = model->addOperand(&type24);
+  auto in1 = model->addOperand(&type41);
+  auto roi1 = model->addOperand(&type27);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
@@ -929,7 +929,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type39);
+  auto out1 = model->addOperand(&type42);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -962,24 +962,24 @@
 
 void CreateModel_nhwc_float16_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type40(Type::TENSOR_FLOAT16, {4, 4, 8, 2});
-  OperandType type41(Type::TENSOR_FLOAT16, {4, 2, 3, 2});
+  OperandType type43(Type::TENSOR_FLOAT16, {4, 4, 8, 2});
+  OperandType type44(Type::TENSOR_FLOAT16, {4, 2, 3, 2});
   OperandType type5(Type::INT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type40);
-  auto roi1 = model->addOperand(&type28);
+  auto in1 = model->addOperand(&type43);
+  auto roi1 = model->addOperand(&type31);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
-  auto param10 = model->addOperand(&type27);
-  auto param11 = model->addOperand(&type27);
+  auto param10 = model->addOperand(&type30);
+  auto param11 = model->addOperand(&type30);
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type41);
+  auto out1 = model->addOperand(&type44);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1014,12 +1014,12 @@
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type42(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
-  OperandType type43(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
+  OperandType type45(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
+  OperandType type46(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type42);
+  auto in1 = model->addOperand(&type45);
   auto roi1 = model->addOperand(&type2);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
@@ -1029,7 +1029,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type43);
+  auto out1 = model->addOperand(&type46);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1064,12 +1064,12 @@
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type42(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
-  OperandType type43(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
+  OperandType type45(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
+  OperandType type46(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type42);
+  auto in1 = model->addOperand(&type45);
   auto roi1 = model->addOperand(&type2);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
@@ -1079,7 +1079,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type43);
+  auto out1 = model->addOperand(&type46);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1114,15 +1114,15 @@
 
 void CreateModel_nchw_quant8_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type44(Type::TENSOR_QUANT8_ASYMM, {4, 2, 4, 8}, 0.04f, 0);
-  OperandType type45(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 3}, 0.03125f, 10);
+  OperandType type47(Type::TENSOR_QUANT8_ASYMM, {4, 2, 4, 8}, 0.04f, 0);
+  OperandType type48(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 3}, 0.03125f, 10);
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type44);
-  auto roi1 = model->addOperand(&type24);
+  auto in1 = model->addOperand(&type47);
+  auto roi1 = model->addOperand(&type27);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
@@ -1131,7 +1131,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type45);
+  auto out1 = model->addOperand(&type48);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1164,24 +1164,24 @@
 
 void CreateModel_nchw_float16_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type46(Type::TENSOR_FLOAT16, {4, 2, 4, 8});
-  OperandType type47(Type::TENSOR_FLOAT16, {4, 2, 2, 3});
+  OperandType type49(Type::TENSOR_FLOAT16, {4, 2, 4, 8});
   OperandType type5(Type::INT32, {});
+  OperandType type50(Type::TENSOR_FLOAT16, {4, 2, 2, 3});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type46);
-  auto roi1 = model->addOperand(&type28);
+  auto in1 = model->addOperand(&type49);
+  auto roi1 = model->addOperand(&type31);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
-  auto param10 = model->addOperand(&type27);
-  auto param11 = model->addOperand(&type27);
+  auto param10 = model->addOperand(&type30);
+  auto param11 = model->addOperand(&type30);
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type47);
+  auto out1 = model->addOperand(&type50);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1215,7 +1215,7 @@
 void CreateModel_dynamic_output_shape_nhwc_2(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
@@ -1231,7 +1231,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type35);
+  auto out1 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1265,7 +1265,7 @@
 void CreateModel_dynamic_output_shape_nhwc_relaxed_2(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
@@ -1281,7 +1281,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type35);
+  auto out1 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1316,15 +1316,15 @@
 
 void CreateModel_dynamic_output_shape_nhwc_quant8_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
-  OperandType type38(Type::TENSOR_QUANT8_ASYMM, {4, 4, 8, 2}, 0.04f, 0);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type48(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
+  OperandType type41(Type::TENSOR_QUANT8_ASYMM, {4, 4, 8, 2}, 0.04f, 0);
   OperandType type5(Type::INT32, {});
+  OperandType type51(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type38);
-  auto roi1 = model->addOperand(&type24);
+  auto in1 = model->addOperand(&type41);
+  auto roi1 = model->addOperand(&type27);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
@@ -1333,7 +1333,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type48);
+  auto out1 = model->addOperand(&type51);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1366,24 +1366,24 @@
 
 void CreateModel_dynamic_output_shape_nhwc_float16_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type40(Type::TENSOR_FLOAT16, {4, 4, 8, 2});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type43(Type::TENSOR_FLOAT16, {4, 4, 8, 2});
   OperandType type5(Type::INT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type40);
-  auto roi1 = model->addOperand(&type28);
+  auto in1 = model->addOperand(&type43);
+  auto roi1 = model->addOperand(&type31);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
-  auto param10 = model->addOperand(&type27);
-  auto param11 = model->addOperand(&type27);
+  auto param10 = model->addOperand(&type30);
+  auto param11 = model->addOperand(&type30);
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type37);
+  auto out1 = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1417,13 +1417,13 @@
 void CreateModel_dynamic_output_shape_nchw_2(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type42(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
+  OperandType type45(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type42);
+  auto in1 = model->addOperand(&type45);
   auto roi1 = model->addOperand(&type2);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
@@ -1433,7 +1433,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type35);
+  auto out1 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1467,13 +1467,13 @@
 void CreateModel_dynamic_output_shape_nchw_relaxed_2(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type42(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
+  OperandType type45(Type::TENSOR_FLOAT32, {4, 2, 4, 8});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type42);
+  auto in1 = model->addOperand(&type45);
   auto roi1 = model->addOperand(&type2);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
@@ -1483,7 +1483,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type35);
+  auto out1 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1518,15 +1518,15 @@
 
 void CreateModel_dynamic_output_shape_nchw_quant8_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type44(Type::TENSOR_QUANT8_ASYMM, {4, 2, 4, 8}, 0.04f, 0);
-  OperandType type48(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
+  OperandType type47(Type::TENSOR_QUANT8_ASYMM, {4, 2, 4, 8}, 0.04f, 0);
   OperandType type5(Type::INT32, {});
+  OperandType type51(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type44);
-  auto roi1 = model->addOperand(&type24);
+  auto in1 = model->addOperand(&type47);
+  auto roi1 = model->addOperand(&type27);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
@@ -1535,7 +1535,7 @@
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type48);
+  auto out1 = model->addOperand(&type51);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1568,24 +1568,24 @@
 
 void CreateModel_dynamic_output_shape_nchw_float16_2(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type46(Type::TENSOR_FLOAT16, {4, 2, 4, 8});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type49(Type::TENSOR_FLOAT16, {4, 2, 4, 8});
   OperandType type5(Type::INT32, {});
   // Phase 1, operands
-  auto in1 = model->addOperand(&type46);
-  auto roi1 = model->addOperand(&type28);
+  auto in1 = model->addOperand(&type49);
+  auto roi1 = model->addOperand(&type31);
   auto param7 = model->addOperand(&type4);
   auto param8 = model->addOperand(&type5);
   auto param9 = model->addOperand(&type5);
-  auto param10 = model->addOperand(&type27);
-  auto param11 = model->addOperand(&type27);
+  auto param10 = model->addOperand(&type30);
+  auto param11 = model->addOperand(&type30);
   auto param12 = model->addOperand(&type5);
   auto param13 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out1 = model->addOperand(&type37);
+  auto out1 = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param7_init[] = {0, 0, 3, 3};
   model->setOperandValue(param7, param7_init, sizeof(int32_t) * 4);
@@ -1720,15 +1720,15 @@
 
 void CreateModel_nhwc_quant8_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
-  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {4, 2, 3, 2}, 0.03125f, 10);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type49(Type::TENSOR_QUANT8_ASYMM, {2, 4, 8, 2}, 0.04f, 0);
+  OperandType type42(Type::TENSOR_QUANT8_ASYMM, {4, 2, 3, 2}, 0.03125f, 10);
   OperandType type5(Type::INT32, {});
+  OperandType type52(Type::TENSOR_QUANT8_ASYMM, {2, 4, 8, 2}, 0.04f, 0);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type49);
-  auto roi2 = model->addOperand(&type24);
+  auto in2 = model->addOperand(&type52);
+  auto roi2 = model->addOperand(&type27);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
@@ -1737,7 +1737,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type39);
+  auto out2 = model->addOperand(&type42);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -1770,24 +1770,24 @@
 
 void CreateModel_nhwc_float16_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type41(Type::TENSOR_FLOAT16, {4, 2, 3, 2});
+  OperandType type44(Type::TENSOR_FLOAT16, {4, 2, 3, 2});
   OperandType type5(Type::INT32, {});
-  OperandType type50(Type::TENSOR_FLOAT16, {2, 4, 8, 2});
+  OperandType type53(Type::TENSOR_FLOAT16, {2, 4, 8, 2});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type50);
-  auto roi2 = model->addOperand(&type28);
+  auto in2 = model->addOperand(&type53);
+  auto roi2 = model->addOperand(&type31);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
-  auto param17 = model->addOperand(&type27);
-  auto param18 = model->addOperand(&type27);
+  auto param17 = model->addOperand(&type30);
+  auto param18 = model->addOperand(&type30);
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type41);
+  auto out2 = model->addOperand(&type44);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -1822,12 +1822,12 @@
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type43(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
+  OperandType type46(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
   OperandType type5(Type::INT32, {});
-  OperandType type51(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
+  OperandType type54(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type51);
+  auto in2 = model->addOperand(&type54);
   auto roi2 = model->addOperand(&type2);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
@@ -1837,7 +1837,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type43);
+  auto out2 = model->addOperand(&type46);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -1872,12 +1872,12 @@
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type43(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
+  OperandType type46(Type::TENSOR_FLOAT32, {4, 2, 2, 3});
   OperandType type5(Type::INT32, {});
-  OperandType type51(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
+  OperandType type54(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type51);
+  auto in2 = model->addOperand(&type54);
   auto roi2 = model->addOperand(&type2);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
@@ -1887,7 +1887,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type43);
+  auto out2 = model->addOperand(&type46);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -1922,15 +1922,15 @@
 
 void CreateModel_nchw_quant8_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type45(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 3}, 0.03125f, 10);
+  OperandType type48(Type::TENSOR_QUANT8_ASYMM, {4, 2, 2, 3}, 0.03125f, 10);
   OperandType type5(Type::INT32, {});
-  OperandType type52(Type::TENSOR_QUANT8_ASYMM, {2, 2, 4, 8}, 0.04f, 0);
+  OperandType type55(Type::TENSOR_QUANT8_ASYMM, {2, 2, 4, 8}, 0.04f, 0);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type52);
-  auto roi2 = model->addOperand(&type24);
+  auto in2 = model->addOperand(&type55);
+  auto roi2 = model->addOperand(&type27);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
@@ -1939,7 +1939,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type45);
+  auto out2 = model->addOperand(&type48);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -1972,24 +1972,24 @@
 
 void CreateModel_nchw_float16_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type47(Type::TENSOR_FLOAT16, {4, 2, 2, 3});
   OperandType type5(Type::INT32, {});
-  OperandType type53(Type::TENSOR_FLOAT16, {2, 2, 4, 8});
+  OperandType type50(Type::TENSOR_FLOAT16, {4, 2, 2, 3});
+  OperandType type56(Type::TENSOR_FLOAT16, {2, 2, 4, 8});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type53);
-  auto roi2 = model->addOperand(&type28);
+  auto in2 = model->addOperand(&type56);
+  auto roi2 = model->addOperand(&type31);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
-  auto param17 = model->addOperand(&type27);
-  auto param18 = model->addOperand(&type27);
+  auto param17 = model->addOperand(&type30);
+  auto param18 = model->addOperand(&type30);
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type47);
+  auto out2 = model->addOperand(&type50);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2023,7 +2023,7 @@
 void CreateModel_dynamic_output_shape_nhwc_3(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
@@ -2039,7 +2039,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type35);
+  auto out2 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2073,7 +2073,7 @@
 void CreateModel_dynamic_output_shape_nhwc_relaxed_3(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
@@ -2089,7 +2089,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type35);
+  auto out2 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2124,15 +2124,15 @@
 
 void CreateModel_dynamic_output_shape_nhwc_quant8_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type48(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
-  OperandType type49(Type::TENSOR_QUANT8_ASYMM, {2, 4, 8, 2}, 0.04f, 0);
   OperandType type5(Type::INT32, {});
+  OperandType type51(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
+  OperandType type52(Type::TENSOR_QUANT8_ASYMM, {2, 4, 8, 2}, 0.04f, 0);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type49);
-  auto roi2 = model->addOperand(&type24);
+  auto in2 = model->addOperand(&type52);
+  auto roi2 = model->addOperand(&type27);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
@@ -2141,7 +2141,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type48);
+  auto out2 = model->addOperand(&type51);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2174,24 +2174,24 @@
 
 void CreateModel_dynamic_output_shape_nhwc_float16_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
-  OperandType type50(Type::TENSOR_FLOAT16, {2, 4, 8, 2});
+  OperandType type53(Type::TENSOR_FLOAT16, {2, 4, 8, 2});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type50);
-  auto roi2 = model->addOperand(&type28);
+  auto in2 = model->addOperand(&type53);
+  auto roi2 = model->addOperand(&type31);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
-  auto param17 = model->addOperand(&type27);
-  auto param18 = model->addOperand(&type27);
+  auto param17 = model->addOperand(&type30);
+  auto param18 = model->addOperand(&type30);
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type37);
+  auto out2 = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2225,13 +2225,13 @@
 void CreateModel_dynamic_output_shape_nchw_3(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
-  OperandType type51(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
+  OperandType type54(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type51);
+  auto in2 = model->addOperand(&type54);
   auto roi2 = model->addOperand(&type2);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
@@ -2241,7 +2241,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type35);
+  auto out2 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2275,13 +2275,13 @@
 void CreateModel_dynamic_output_shape_nchw_relaxed_3(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type4(Type::TENSOR_INT32, {4});
   OperandType type5(Type::INT32, {});
-  OperandType type51(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
+  OperandType type54(Type::TENSOR_FLOAT32, {2, 2, 4, 8});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type51);
+  auto in2 = model->addOperand(&type54);
   auto roi2 = model->addOperand(&type2);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
@@ -2291,7 +2291,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type35);
+  auto out2 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2326,15 +2326,15 @@
 
 void CreateModel_dynamic_output_shape_nchw_quant8_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type24(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
+  OperandType type27(Type::TENSOR_QUANT16_ASYMM, {4, 4}, 0.125f, 0);
   OperandType type4(Type::TENSOR_INT32, {4});
-  OperandType type48(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
   OperandType type5(Type::INT32, {});
-  OperandType type52(Type::TENSOR_QUANT8_ASYMM, {2, 2, 4, 8}, 0.04f, 0);
+  OperandType type51(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.03125f, 10);
+  OperandType type55(Type::TENSOR_QUANT8_ASYMM, {2, 2, 4, 8}, 0.04f, 0);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type52);
-  auto roi2 = model->addOperand(&type24);
+  auto in2 = model->addOperand(&type55);
+  auto roi2 = model->addOperand(&type27);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
@@ -2343,7 +2343,7 @@
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type48);
+  auto out2 = model->addOperand(&type51);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2376,24 +2376,24 @@
 
 void CreateModel_dynamic_output_shape_nchw_float16_3(Model *model) {
   OperandType type0(Type::BOOL, {});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type28(Type::TENSOR_FLOAT16, {4, 4});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type31(Type::TENSOR_FLOAT16, {4, 4});
   OperandType type4(Type::TENSOR_INT32, {4});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
-  OperandType type53(Type::TENSOR_FLOAT16, {2, 2, 4, 8});
+  OperandType type56(Type::TENSOR_FLOAT16, {2, 2, 4, 8});
   // Phase 1, operands
-  auto in2 = model->addOperand(&type53);
-  auto roi2 = model->addOperand(&type28);
+  auto in2 = model->addOperand(&type56);
+  auto roi2 = model->addOperand(&type31);
   auto param14 = model->addOperand(&type4);
   auto param15 = model->addOperand(&type5);
   auto param16 = model->addOperand(&type5);
-  auto param17 = model->addOperand(&type27);
-  auto param18 = model->addOperand(&type27);
+  auto param17 = model->addOperand(&type30);
+  auto param18 = model->addOperand(&type30);
   auto param19 = model->addOperand(&type5);
   auto param20 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out2 = model->addOperand(&type37);
+  auto out2 = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param14_init[] = {0, 0, 1, 1};
   model->setOperandValue(param14, param14_init, sizeof(int32_t) * 4);
@@ -2530,13 +2530,13 @@
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
   OperandType type5(Type::INT32, {});
-  OperandType type54(Type::TENSOR_QUANT8_ASYMM, {4, 4, 4, 1}, 0.25f, 128);
-  OperandType type55(Type::TENSOR_QUANT8_ASYMM, {5, 2, 2, 1}, 0.0625f, 128);
-  OperandType type56(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
+  OperandType type57(Type::TENSOR_QUANT8_ASYMM, {4, 4, 4, 1}, 0.25f, 128);
+  OperandType type58(Type::TENSOR_QUANT8_ASYMM, {5, 2, 2, 1}, 0.0625f, 128);
+  OperandType type59(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type54);
-  auto roi3 = model->addOperand(&type56);
+  auto in3 = model->addOperand(&type57);
+  auto roi3 = model->addOperand(&type59);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
@@ -2545,7 +2545,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type55);
+  auto out3 = model->addOperand(&type58);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2579,23 +2579,23 @@
 void CreateModel_nhwc_float16_4(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type27(Type::FLOAT16, {});
+  OperandType type30(Type::FLOAT16, {});
   OperandType type5(Type::INT32, {});
-  OperandType type57(Type::TENSOR_FLOAT16, {4, 4, 4, 1});
-  OperandType type58(Type::TENSOR_FLOAT16, {5, 2, 2, 1});
-  OperandType type59(Type::TENSOR_FLOAT16, {5, 4});
+  OperandType type60(Type::TENSOR_FLOAT16, {4, 4, 4, 1});
+  OperandType type61(Type::TENSOR_FLOAT16, {5, 2, 2, 1});
+  OperandType type62(Type::TENSOR_FLOAT16, {5, 4});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type57);
-  auto roi3 = model->addOperand(&type59);
+  auto in3 = model->addOperand(&type60);
+  auto roi3 = model->addOperand(&type62);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
-  auto param24 = model->addOperand(&type27);
-  auto param25 = model->addOperand(&type27);
+  auto param24 = model->addOperand(&type30);
+  auto param25 = model->addOperand(&type30);
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type58);
+  auto out3 = model->addOperand(&type61);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2632,10 +2632,10 @@
   OperandType type13(Type::TENSOR_INT32, {5});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type60(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
-  OperandType type61(Type::TENSOR_FLOAT32, {5, 1, 2, 2});
+  OperandType type63(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
+  OperandType type64(Type::TENSOR_FLOAT32, {5, 1, 2, 2});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type60);
+  auto in3 = model->addOperand(&type63);
   auto roi3 = model->addOperand(&type11);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
@@ -2645,7 +2645,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type61);
+  auto out3 = model->addOperand(&type64);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2682,10 +2682,10 @@
   OperandType type13(Type::TENSOR_INT32, {5});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type60(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
-  OperandType type61(Type::TENSOR_FLOAT32, {5, 1, 2, 2});
+  OperandType type63(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
+  OperandType type64(Type::TENSOR_FLOAT32, {5, 1, 2, 2});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type60);
+  auto in3 = model->addOperand(&type63);
   auto roi3 = model->addOperand(&type11);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
@@ -2695,7 +2695,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type61);
+  auto out3 = model->addOperand(&type64);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2732,13 +2732,13 @@
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
   OperandType type5(Type::INT32, {});
-  OperandType type56(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
+  OperandType type59(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
   OperandType type6(Type::FLOAT32, {});
-  OperandType type62(Type::TENSOR_QUANT8_ASYMM, {4, 1, 4, 4}, 0.25f, 128);
-  OperandType type63(Type::TENSOR_QUANT8_ASYMM, {5, 1, 2, 2}, 0.0625f, 128);
+  OperandType type65(Type::TENSOR_QUANT8_ASYMM, {4, 1, 4, 4}, 0.25f, 128);
+  OperandType type66(Type::TENSOR_QUANT8_ASYMM, {5, 1, 2, 2}, 0.0625f, 128);
   // Phase 1, operands
-  auto in3 = model->addOperand(&type62);
-  auto roi3 = model->addOperand(&type56);
+  auto in3 = model->addOperand(&type65);
+  auto roi3 = model->addOperand(&type59);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
@@ -2747,7 +2747,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type63);
+  auto out3 = model->addOperand(&type66);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2781,23 +2781,23 @@
 void CreateModel_nchw_float16_4(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type27(Type::FLOAT16, {});
+  OperandType type30(Type::FLOAT16, {});
   OperandType type5(Type::INT32, {});
-  OperandType type59(Type::TENSOR_FLOAT16, {5, 4});
-  OperandType type64(Type::TENSOR_FLOAT16, {4, 1, 4, 4});
-  OperandType type65(Type::TENSOR_FLOAT16, {5, 1, 2, 2});
+  OperandType type62(Type::TENSOR_FLOAT16, {5, 4});
+  OperandType type67(Type::TENSOR_FLOAT16, {4, 1, 4, 4});
+  OperandType type68(Type::TENSOR_FLOAT16, {5, 1, 2, 2});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type64);
-  auto roi3 = model->addOperand(&type59);
+  auto in3 = model->addOperand(&type67);
+  auto roi3 = model->addOperand(&type62);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
-  auto param24 = model->addOperand(&type27);
-  auto param25 = model->addOperand(&type27);
+  auto param24 = model->addOperand(&type30);
+  auto param25 = model->addOperand(&type30);
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type65);
+  auto out3 = model->addOperand(&type68);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2833,7 +2833,7 @@
   OperandType type10(Type::TENSOR_FLOAT32, {4, 4, 4, 1});
   OperandType type11(Type::TENSOR_FLOAT32, {5, 4});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
@@ -2847,7 +2847,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type35);
+  auto out3 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2883,7 +2883,7 @@
   OperandType type10(Type::TENSOR_FLOAT32, {4, 4, 4, 1});
   OperandType type11(Type::TENSOR_FLOAT32, {5, 4});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
@@ -2897,7 +2897,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type35);
+  auto out3 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2933,14 +2933,14 @@
 void CreateModel_dynamic_output_shape_nhwc_quant8_4(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type36(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
+  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
   OperandType type5(Type::INT32, {});
-  OperandType type54(Type::TENSOR_QUANT8_ASYMM, {4, 4, 4, 1}, 0.25f, 128);
-  OperandType type56(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
+  OperandType type57(Type::TENSOR_QUANT8_ASYMM, {4, 4, 4, 1}, 0.25f, 128);
+  OperandType type59(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type54);
-  auto roi3 = model->addOperand(&type56);
+  auto in3 = model->addOperand(&type57);
+  auto roi3 = model->addOperand(&type59);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
@@ -2949,7 +2949,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type36);
+  auto out3 = model->addOperand(&type39);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -2983,23 +2983,23 @@
 void CreateModel_dynamic_output_shape_nhwc_float16_4(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
-  OperandType type57(Type::TENSOR_FLOAT16, {4, 4, 4, 1});
-  OperandType type59(Type::TENSOR_FLOAT16, {5, 4});
+  OperandType type60(Type::TENSOR_FLOAT16, {4, 4, 4, 1});
+  OperandType type62(Type::TENSOR_FLOAT16, {5, 4});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type57);
-  auto roi3 = model->addOperand(&type59);
+  auto in3 = model->addOperand(&type60);
+  auto roi3 = model->addOperand(&type62);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
-  auto param24 = model->addOperand(&type27);
-  auto param25 = model->addOperand(&type27);
+  auto param24 = model->addOperand(&type30);
+  auto param25 = model->addOperand(&type30);
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type37);
+  auto out3 = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -3034,12 +3034,12 @@
   OperandType type0(Type::BOOL, {});
   OperandType type11(Type::TENSOR_FLOAT32, {5, 4});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type60(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
+  OperandType type63(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type60);
+  auto in3 = model->addOperand(&type63);
   auto roi3 = model->addOperand(&type11);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
@@ -3049,7 +3049,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type35);
+  auto out3 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -3084,12 +3084,12 @@
   OperandType type0(Type::BOOL, {});
   OperandType type11(Type::TENSOR_FLOAT32, {5, 4});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type60(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
+  OperandType type63(Type::TENSOR_FLOAT32, {4, 1, 4, 4});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type60);
+  auto in3 = model->addOperand(&type63);
   auto roi3 = model->addOperand(&type11);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
@@ -3099,7 +3099,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type35);
+  auto out3 = model->addOperand(&type38);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -3135,14 +3135,14 @@
 void CreateModel_dynamic_output_shape_nchw_quant8_4(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type36(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
+  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
   OperandType type5(Type::INT32, {});
-  OperandType type56(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
+  OperandType type59(Type::TENSOR_QUANT16_ASYMM, {5, 4}, 0.125f, 0);
   OperandType type6(Type::FLOAT32, {});
-  OperandType type62(Type::TENSOR_QUANT8_ASYMM, {4, 1, 4, 4}, 0.25f, 128);
+  OperandType type65(Type::TENSOR_QUANT8_ASYMM, {4, 1, 4, 4}, 0.25f, 128);
   // Phase 1, operands
-  auto in3 = model->addOperand(&type62);
-  auto roi3 = model->addOperand(&type56);
+  auto in3 = model->addOperand(&type65);
+  auto roi3 = model->addOperand(&type59);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
@@ -3151,7 +3151,7 @@
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type36);
+  auto out3 = model->addOperand(&type39);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -3185,23 +3185,23 @@
 void CreateModel_dynamic_output_shape_nchw_float16_4(Model *model) {
   OperandType type0(Type::BOOL, {});
   OperandType type13(Type::TENSOR_INT32, {5});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
-  OperandType type59(Type::TENSOR_FLOAT16, {5, 4});
-  OperandType type64(Type::TENSOR_FLOAT16, {4, 1, 4, 4});
+  OperandType type62(Type::TENSOR_FLOAT16, {5, 4});
+  OperandType type67(Type::TENSOR_FLOAT16, {4, 1, 4, 4});
   // Phase 1, operands
-  auto in3 = model->addOperand(&type64);
-  auto roi3 = model->addOperand(&type59);
+  auto in3 = model->addOperand(&type67);
+  auto roi3 = model->addOperand(&type62);
   auto param21 = model->addOperand(&type13);
   auto param22 = model->addOperand(&type5);
   auto param23 = model->addOperand(&type5);
-  auto param24 = model->addOperand(&type27);
-  auto param25 = model->addOperand(&type27);
+  auto param24 = model->addOperand(&type30);
+  auto param25 = model->addOperand(&type30);
   auto param26 = model->addOperand(&type5);
   auto param27 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto out3 = model->addOperand(&type37);
+  auto out3 = model->addOperand(&type40);
   // Phase 2, operations
   static int32_t param21_init[] = {2, 2, 2, 2, 2};
   model->setOperandValue(param21, param21_init, sizeof(int32_t) * 5);
@@ -3404,15 +3404,15 @@
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type66(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128);
-  OperandType type67(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
-  OperandType type68(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
-  OperandType type69(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
-  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
-  OperandType type71(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
+  OperandType type69(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128);
+  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
+  OperandType type71(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
+  OperandType type72(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
+  OperandType type73(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
+  OperandType type74(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
   // Phase 1, operands
-  auto scores = model->addOperand(&type70);
-  auto roi4 = model->addOperand(&type68);
+  auto scores = model->addOperand(&type73);
+  auto roi4 = model->addOperand(&type71);
   auto param28 = model->addOperand(&type19);
   auto param29 = model->addOperand(&type6);
   auto param30 = model->addOperand(&type5);
@@ -3420,11 +3420,11 @@
   auto param32 = model->addOperand(&type6);
   auto param33 = model->addOperand(&type6);
   auto param34 = model->addOperand(&type6);
-  auto scoresOut = model->addOperand(&type71);
-  auto roiOut = model->addOperand(&type69);
+  auto scoresOut = model->addOperand(&type74);
+  auto roiOut = model->addOperand(&type72);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type67);
+  auto in4 = model->addOperand(&type70);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
   auto param37 = model->addOperand(&type6);
@@ -3432,7 +3432,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type66);
+  auto featureMap = model->addOperand(&type69);
   // Phase 2, operations
   static uint8_t scores_init[] = {137, 129};
   model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2);
@@ -3484,37 +3484,37 @@
   OperandType type0(Type::BOOL, {});
   OperandType type17(Type::TENSOR_INT32, {0});
   OperandType type19(Type::TENSOR_INT32, {1});
-  OperandType type27(Type::FLOAT16, {});
+  OperandType type30(Type::FLOAT16, {});
   OperandType type5(Type::INT32, {});
-  OperandType type72(Type::TENSOR_FLOAT16, {0, 2, 2, 1});
-  OperandType type73(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
-  OperandType type74(Type::TENSOR_FLOAT16, {1, 8});
-  OperandType type75(Type::TENSOR_FLOAT16, {0, 4});
-  OperandType type76(Type::TENSOR_FLOAT16, {1, 2});
-  OperandType type77(Type::TENSOR_FLOAT16, {0});
+  OperandType type75(Type::TENSOR_FLOAT16, {0, 2, 2, 1});
+  OperandType type76(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
+  OperandType type77(Type::TENSOR_FLOAT16, {1, 8});
+  OperandType type78(Type::TENSOR_FLOAT16, {0, 4});
+  OperandType type79(Type::TENSOR_FLOAT16, {1, 2});
+  OperandType type80(Type::TENSOR_FLOAT16, {0});
   // Phase 1, operands
-  auto scores = model->addOperand(&type76);
-  auto roi4 = model->addOperand(&type74);
+  auto scores = model->addOperand(&type79);
+  auto roi4 = model->addOperand(&type77);
   auto param28 = model->addOperand(&type19);
-  auto param29 = model->addOperand(&type27);
+  auto param29 = model->addOperand(&type30);
   auto param30 = model->addOperand(&type5);
   auto param31 = model->addOperand(&type5);
-  auto param32 = model->addOperand(&type27);
-  auto param33 = model->addOperand(&type27);
-  auto param34 = model->addOperand(&type27);
-  auto scoresOut = model->addOperand(&type77);
-  auto roiOut = model->addOperand(&type75);
+  auto param32 = model->addOperand(&type30);
+  auto param33 = model->addOperand(&type30);
+  auto param34 = model->addOperand(&type30);
+  auto scoresOut = model->addOperand(&type80);
+  auto roiOut = model->addOperand(&type78);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type73);
+  auto in4 = model->addOperand(&type76);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
-  auto param37 = model->addOperand(&type27);
-  auto param38 = model->addOperand(&type27);
+  auto param37 = model->addOperand(&type30);
+  auto param38 = model->addOperand(&type30);
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type72);
+  auto featureMap = model->addOperand(&type75);
   // Phase 2, operations
   static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
   model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
@@ -3573,7 +3573,7 @@
   OperandType type20(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type78(Type::TENSOR_FLOAT32, {0, 1, 2, 2});
+  OperandType type81(Type::TENSOR_FLOAT32, {0, 1, 2, 2});
   // Phase 1, operands
   auto scores = model->addOperand(&type14);
   auto roi4 = model->addOperand(&type15);
@@ -3596,7 +3596,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type78);
+  auto featureMap = model->addOperand(&type81);
   // Phase 2, operations
   static float scores_init[] = {0.9f, 0.1f};
   model->setOperandValue(scores, scores_init, sizeof(float) * 2);
@@ -3655,7 +3655,7 @@
   OperandType type20(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type78(Type::TENSOR_FLOAT32, {0, 1, 2, 2});
+  OperandType type81(Type::TENSOR_FLOAT32, {0, 1, 2, 2});
   // Phase 1, operands
   auto scores = model->addOperand(&type14);
   auto roi4 = model->addOperand(&type15);
@@ -3678,7 +3678,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type78);
+  auto featureMap = model->addOperand(&type81);
   // Phase 2, operations
   static float scores_init[] = {0.9f, 0.1f};
   model->setOperandValue(scores, scores_init, sizeof(float) * 2);
@@ -3734,15 +3734,15 @@
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type67(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
-  OperandType type68(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
-  OperandType type69(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
-  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
-  OperandType type71(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
-  OperandType type79(Type::TENSOR_QUANT8_ASYMM, {0, 1, 2, 2}, 0.1f, 128);
+  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
+  OperandType type71(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
+  OperandType type72(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
+  OperandType type73(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
+  OperandType type74(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
+  OperandType type82(Type::TENSOR_QUANT8_ASYMM, {0, 1, 2, 2}, 0.1f, 128);
   // Phase 1, operands
-  auto scores = model->addOperand(&type70);
-  auto roi4 = model->addOperand(&type68);
+  auto scores = model->addOperand(&type73);
+  auto roi4 = model->addOperand(&type71);
   auto param28 = model->addOperand(&type19);
   auto param29 = model->addOperand(&type6);
   auto param30 = model->addOperand(&type5);
@@ -3750,11 +3750,11 @@
   auto param32 = model->addOperand(&type6);
   auto param33 = model->addOperand(&type6);
   auto param34 = model->addOperand(&type6);
-  auto scoresOut = model->addOperand(&type71);
-  auto roiOut = model->addOperand(&type69);
+  auto scoresOut = model->addOperand(&type74);
+  auto roiOut = model->addOperand(&type72);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type67);
+  auto in4 = model->addOperand(&type70);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
   auto param37 = model->addOperand(&type6);
@@ -3762,7 +3762,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type79);
+  auto featureMap = model->addOperand(&type82);
   // Phase 2, operations
   static uint8_t scores_init[] = {137, 129};
   model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2);
@@ -3814,37 +3814,37 @@
   OperandType type0(Type::BOOL, {});
   OperandType type17(Type::TENSOR_INT32, {0});
   OperandType type19(Type::TENSOR_INT32, {1});
-  OperandType type27(Type::FLOAT16, {});
+  OperandType type30(Type::FLOAT16, {});
   OperandType type5(Type::INT32, {});
-  OperandType type73(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
-  OperandType type74(Type::TENSOR_FLOAT16, {1, 8});
-  OperandType type75(Type::TENSOR_FLOAT16, {0, 4});
-  OperandType type76(Type::TENSOR_FLOAT16, {1, 2});
-  OperandType type77(Type::TENSOR_FLOAT16, {0});
-  OperandType type80(Type::TENSOR_FLOAT16, {0, 1, 2, 2});
+  OperandType type76(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
+  OperandType type77(Type::TENSOR_FLOAT16, {1, 8});
+  OperandType type78(Type::TENSOR_FLOAT16, {0, 4});
+  OperandType type79(Type::TENSOR_FLOAT16, {1, 2});
+  OperandType type80(Type::TENSOR_FLOAT16, {0});
+  OperandType type83(Type::TENSOR_FLOAT16, {0, 1, 2, 2});
   // Phase 1, operands
-  auto scores = model->addOperand(&type76);
-  auto roi4 = model->addOperand(&type74);
+  auto scores = model->addOperand(&type79);
+  auto roi4 = model->addOperand(&type77);
   auto param28 = model->addOperand(&type19);
-  auto param29 = model->addOperand(&type27);
+  auto param29 = model->addOperand(&type30);
   auto param30 = model->addOperand(&type5);
   auto param31 = model->addOperand(&type5);
-  auto param32 = model->addOperand(&type27);
-  auto param33 = model->addOperand(&type27);
-  auto param34 = model->addOperand(&type27);
-  auto scoresOut = model->addOperand(&type77);
-  auto roiOut = model->addOperand(&type75);
+  auto param32 = model->addOperand(&type30);
+  auto param33 = model->addOperand(&type30);
+  auto param34 = model->addOperand(&type30);
+  auto scoresOut = model->addOperand(&type80);
+  auto roiOut = model->addOperand(&type78);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type73);
+  auto in4 = model->addOperand(&type76);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
-  auto param37 = model->addOperand(&type27);
-  auto param38 = model->addOperand(&type27);
+  auto param37 = model->addOperand(&type30);
+  auto param38 = model->addOperand(&type30);
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type80);
+  auto featureMap = model->addOperand(&type83);
   // Phase 2, operations
   static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
   model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
@@ -3901,7 +3901,7 @@
   OperandType type18(Type::TENSOR_FLOAT32, {0, 4});
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type20(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
@@ -3926,7 +3926,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type35);
+  auto featureMap = model->addOperand(&type38);
   // Phase 2, operations
   static float scores_init[] = {0.9f, 0.1f};
   model->setOperandValue(scores, scores_init, sizeof(float) * 2);
@@ -3983,7 +3983,7 @@
   OperandType type18(Type::TENSOR_FLOAT32, {0, 4});
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type20(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
@@ -4008,7 +4008,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type35);
+  auto featureMap = model->addOperand(&type38);
   // Phase 2, operations
   static float scores_init[] = {0.9f, 0.1f};
   model->setOperandValue(scores, scores_init, sizeof(float) * 2);
@@ -4064,15 +4064,15 @@
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type67(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
-  OperandType type68(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
-  OperandType type69(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
-  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
-  OperandType type71(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
-  OperandType type81(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
+  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
+  OperandType type71(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
+  OperandType type72(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
+  OperandType type73(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
+  OperandType type74(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
+  OperandType type84(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
   // Phase 1, operands
-  auto scores = model->addOperand(&type70);
-  auto roi4 = model->addOperand(&type68);
+  auto scores = model->addOperand(&type73);
+  auto roi4 = model->addOperand(&type71);
   auto param28 = model->addOperand(&type19);
   auto param29 = model->addOperand(&type6);
   auto param30 = model->addOperand(&type5);
@@ -4080,11 +4080,11 @@
   auto param32 = model->addOperand(&type6);
   auto param33 = model->addOperand(&type6);
   auto param34 = model->addOperand(&type6);
-  auto scoresOut = model->addOperand(&type71);
-  auto roiOut = model->addOperand(&type69);
+  auto scoresOut = model->addOperand(&type74);
+  auto roiOut = model->addOperand(&type72);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type67);
+  auto in4 = model->addOperand(&type70);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
   auto param37 = model->addOperand(&type6);
@@ -4092,7 +4092,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type81);
+  auto featureMap = model->addOperand(&type84);
   // Phase 2, operations
   static uint8_t scores_init[] = {137, 129};
   model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2);
@@ -4144,37 +4144,37 @@
   OperandType type0(Type::BOOL, {});
   OperandType type17(Type::TENSOR_INT32, {0});
   OperandType type19(Type::TENSOR_INT32, {1});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
-  OperandType type73(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
-  OperandType type74(Type::TENSOR_FLOAT16, {1, 8});
-  OperandType type75(Type::TENSOR_FLOAT16, {0, 4});
-  OperandType type76(Type::TENSOR_FLOAT16, {1, 2});
-  OperandType type82(Type::TENSOR_FLOAT16, {0});
+  OperandType type76(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
+  OperandType type77(Type::TENSOR_FLOAT16, {1, 8});
+  OperandType type78(Type::TENSOR_FLOAT16, {0, 4});
+  OperandType type79(Type::TENSOR_FLOAT16, {1, 2});
+  OperandType type85(Type::TENSOR_FLOAT16, {0});
   // Phase 1, operands
-  auto scores = model->addOperand(&type76);
-  auto roi4 = model->addOperand(&type74);
+  auto scores = model->addOperand(&type79);
+  auto roi4 = model->addOperand(&type77);
   auto param28 = model->addOperand(&type19);
-  auto param29 = model->addOperand(&type27);
+  auto param29 = model->addOperand(&type30);
   auto param30 = model->addOperand(&type5);
   auto param31 = model->addOperand(&type5);
-  auto param32 = model->addOperand(&type27);
-  auto param33 = model->addOperand(&type27);
-  auto param34 = model->addOperand(&type27);
-  auto scoresOut = model->addOperand(&type82);
-  auto roiOut = model->addOperand(&type75);
+  auto param32 = model->addOperand(&type30);
+  auto param33 = model->addOperand(&type30);
+  auto param34 = model->addOperand(&type30);
+  auto scoresOut = model->addOperand(&type85);
+  auto roiOut = model->addOperand(&type78);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type73);
+  auto in4 = model->addOperand(&type76);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
-  auto param37 = model->addOperand(&type27);
-  auto param38 = model->addOperand(&type27);
+  auto param37 = model->addOperand(&type30);
+  auto param38 = model->addOperand(&type30);
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type37);
+  auto featureMap = model->addOperand(&type40);
   // Phase 2, operations
   static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
   model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
@@ -4231,7 +4231,7 @@
   OperandType type18(Type::TENSOR_FLOAT32, {0, 4});
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type20(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
@@ -4256,7 +4256,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type35);
+  auto featureMap = model->addOperand(&type38);
   // Phase 2, operations
   static float scores_init[] = {0.9f, 0.1f};
   model->setOperandValue(scores, scores_init, sizeof(float) * 2);
@@ -4313,7 +4313,7 @@
   OperandType type18(Type::TENSOR_FLOAT32, {0, 4});
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type20(Type::TENSOR_FLOAT32, {1, 1, 1, 1});
-  OperandType type35(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
   // Phase 1, operands
@@ -4338,7 +4338,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type35);
+  auto featureMap = model->addOperand(&type38);
   // Phase 2, operations
   static float scores_init[] = {0.9f, 0.1f};
   model->setOperandValue(scores, scores_init, sizeof(float) * 2);
@@ -4394,15 +4394,15 @@
   OperandType type19(Type::TENSOR_INT32, {1});
   OperandType type5(Type::INT32, {});
   OperandType type6(Type::FLOAT32, {});
-  OperandType type67(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
-  OperandType type68(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
-  OperandType type69(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
-  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
-  OperandType type71(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
-  OperandType type81(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
+  OperandType type70(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128);
+  OperandType type71(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0);
+  OperandType type72(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0);
+  OperandType type73(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128);
+  OperandType type74(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128);
+  OperandType type84(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128);
   // Phase 1, operands
-  auto scores = model->addOperand(&type70);
-  auto roi4 = model->addOperand(&type68);
+  auto scores = model->addOperand(&type73);
+  auto roi4 = model->addOperand(&type71);
   auto param28 = model->addOperand(&type19);
   auto param29 = model->addOperand(&type6);
   auto param30 = model->addOperand(&type5);
@@ -4410,11 +4410,11 @@
   auto param32 = model->addOperand(&type6);
   auto param33 = model->addOperand(&type6);
   auto param34 = model->addOperand(&type6);
-  auto scoresOut = model->addOperand(&type71);
-  auto roiOut = model->addOperand(&type69);
+  auto scoresOut = model->addOperand(&type74);
+  auto roiOut = model->addOperand(&type72);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type67);
+  auto in4 = model->addOperand(&type70);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
   auto param37 = model->addOperand(&type6);
@@ -4422,7 +4422,7 @@
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type81);
+  auto featureMap = model->addOperand(&type84);
   // Phase 2, operations
   static uint8_t scores_init[] = {137, 129};
   model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2);
@@ -4474,37 +4474,37 @@
   OperandType type0(Type::BOOL, {});
   OperandType type17(Type::TENSOR_INT32, {0});
   OperandType type19(Type::TENSOR_INT32, {1});
-  OperandType type27(Type::FLOAT16, {});
-  OperandType type37(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
   OperandType type5(Type::INT32, {});
-  OperandType type73(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
-  OperandType type74(Type::TENSOR_FLOAT16, {1, 8});
-  OperandType type75(Type::TENSOR_FLOAT16, {0, 4});
-  OperandType type76(Type::TENSOR_FLOAT16, {1, 2});
-  OperandType type82(Type::TENSOR_FLOAT16, {0});
+  OperandType type76(Type::TENSOR_FLOAT16, {1, 1, 1, 1});
+  OperandType type77(Type::TENSOR_FLOAT16, {1, 8});
+  OperandType type78(Type::TENSOR_FLOAT16, {0, 4});
+  OperandType type79(Type::TENSOR_FLOAT16, {1, 2});
+  OperandType type85(Type::TENSOR_FLOAT16, {0});
   // Phase 1, operands
-  auto scores = model->addOperand(&type76);
-  auto roi4 = model->addOperand(&type74);
+  auto scores = model->addOperand(&type79);
+  auto roi4 = model->addOperand(&type77);
   auto param28 = model->addOperand(&type19);
-  auto param29 = model->addOperand(&type27);
+  auto param29 = model->addOperand(&type30);
   auto param30 = model->addOperand(&type5);
   auto param31 = model->addOperand(&type5);
-  auto param32 = model->addOperand(&type27);
-  auto param33 = model->addOperand(&type27);
-  auto param34 = model->addOperand(&type27);
-  auto scoresOut = model->addOperand(&type82);
-  auto roiOut = model->addOperand(&type75);
+  auto param32 = model->addOperand(&type30);
+  auto param33 = model->addOperand(&type30);
+  auto param34 = model->addOperand(&type30);
+  auto scoresOut = model->addOperand(&type85);
+  auto roiOut = model->addOperand(&type78);
   auto classesOut = model->addOperand(&type17);
   auto batchSplitOut = model->addOperand(&type17);
-  auto in4 = model->addOperand(&type73);
+  auto in4 = model->addOperand(&type76);
   auto param35 = model->addOperand(&type5);
   auto param36 = model->addOperand(&type5);
-  auto param37 = model->addOperand(&type27);
-  auto param38 = model->addOperand(&type27);
+  auto param37 = model->addOperand(&type30);
+  auto param38 = model->addOperand(&type30);
   auto param39 = model->addOperand(&type5);
   auto param40 = model->addOperand(&type5);
   auto layout = model->addOperand(&type0);
-  auto featureMap = model->addOperand(&type37);
+  auto featureMap = model->addOperand(&type40);
   // Phase 2, operations
   static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f};
   model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2);
@@ -4552,3 +4552,811 @@
   return ignore.find(i) != ignore.end();
 }
 
+void CreateModel_nhwc_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type22(Type::TENSOR_FLOAT32, {1, 512, 8, 1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type24(Type::TENSOR_FLOAT32, {1, 128, 4, 1});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type22);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type24);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nhwc_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_nhwc_relaxed_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type22(Type::TENSOR_FLOAT32, {1, 512, 8, 1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type24(Type::TENSOR_FLOAT32, {1, 128, 4, 1});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type22);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type24);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  // Phase 4: set relaxed execution
+  model->relaxComputationFloat32toFloat16(true);
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nhwc_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_nhwc_quant8_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type86(Type::TENSOR_QUANT8_ASYMM, {1, 512, 8, 1}, 0.25f, 128);
+  OperandType type87(Type::TENSOR_QUANT8_ASYMM, {1, 128, 4, 1}, 0.0625f, 128);
+  OperandType type88(Type::TENSOR_QUANT16_ASYMM, {1, 4}, 0.125f, 0);
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type86);
+  auto roi5 = model->addOperand(&type88);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type87);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nhwc_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_nhwc_float16_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type5(Type::INT32, {});
+  OperandType type89(Type::TENSOR_FLOAT16, {1, 512, 8, 1});
+  OperandType type90(Type::TENSOR_FLOAT16, {1, 128, 4, 1});
+  OperandType type91(Type::TENSOR_FLOAT16, {1, 4});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type89);
+  auto roi5 = model->addOperand(&type91);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type30);
+  auto param45 = model->addOperand(&type30);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type90);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static _Float16 param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(_Float16) * 1);
+  static _Float16 param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(_Float16) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nhwc_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_nchw_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type92(Type::TENSOR_FLOAT32, {1, 1, 512, 8});
+  OperandType type93(Type::TENSOR_FLOAT32, {1, 1, 128, 4});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type92);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type93);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nchw_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_nchw_relaxed_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type92(Type::TENSOR_FLOAT32, {1, 1, 512, 8});
+  OperandType type93(Type::TENSOR_FLOAT32, {1, 1, 128, 4});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type92);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type93);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  // Phase 4: set relaxed execution
+  model->relaxComputationFloat32toFloat16(true);
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nchw_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_nchw_quant8_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type88(Type::TENSOR_QUANT16_ASYMM, {1, 4}, 0.125f, 0);
+  OperandType type94(Type::TENSOR_QUANT8_ASYMM, {1, 1, 512, 8}, 0.25f, 128);
+  OperandType type95(Type::TENSOR_QUANT8_ASYMM, {1, 1, 128, 4}, 0.0625f, 128);
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type94);
+  auto roi5 = model->addOperand(&type88);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type95);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nchw_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_nchw_float16_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type5(Type::INT32, {});
+  OperandType type91(Type::TENSOR_FLOAT16, {1, 4});
+  OperandType type96(Type::TENSOR_FLOAT16, {1, 1, 512, 8});
+  OperandType type97(Type::TENSOR_FLOAT16, {1, 1, 128, 4});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type96);
+  auto roi5 = model->addOperand(&type91);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type30);
+  auto param45 = model->addOperand(&type30);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type97);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static _Float16 param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(_Float16) * 1);
+  static _Float16 param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(_Float16) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_nchw_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nhwc_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type22(Type::TENSOR_FLOAT32, {1, 512, 8, 1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type22);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type38);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nhwc_relaxed_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type22(Type::TENSOR_FLOAT32, {1, 512, 8, 1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type22);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type38);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  // Phase 4: set relaxed execution
+  model->relaxComputationFloat32toFloat16(true);
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nhwc_quant8_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type86(Type::TENSOR_QUANT8_ASYMM, {1, 512, 8, 1}, 0.25f, 128);
+  OperandType type88(Type::TENSOR_QUANT16_ASYMM, {1, 4}, 0.125f, 0);
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type86);
+  auto roi5 = model->addOperand(&type88);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type39);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nhwc_float16_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type5(Type::INT32, {});
+  OperandType type89(Type::TENSOR_FLOAT16, {1, 512, 8, 1});
+  OperandType type91(Type::TENSOR_FLOAT16, {1, 4});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type89);
+  auto roi5 = model->addOperand(&type91);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type30);
+  auto param45 = model->addOperand(&type30);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type40);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static _Float16 param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(_Float16) * 1);
+  static _Float16 param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(_Float16) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {false};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nchw_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type92(Type::TENSOR_FLOAT32, {1, 1, 512, 8});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type92);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type38);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nchw_relaxed_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type23(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type38(Type::TENSOR_FLOAT32, {0, 0, 0, 0});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type92(Type::TENSOR_FLOAT32, {1, 1, 512, 8});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type92);
+  auto roi5 = model->addOperand(&type23);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type38);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  // Phase 4: set relaxed execution
+  model->relaxComputationFloat32toFloat16(true);
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nchw_quant8_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type39(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.0625f, 128);
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::FLOAT32, {});
+  OperandType type88(Type::TENSOR_QUANT16_ASYMM, {1, 4}, 0.125f, 0);
+  OperandType type94(Type::TENSOR_QUANT8_ASYMM, {1, 1, 512, 8}, 0.25f, 128);
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type94);
+  auto roi5 = model->addOperand(&type88);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type6);
+  auto param45 = model->addOperand(&type6);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type39);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static float param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(float) * 1);
+  static float param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(float) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape_nchw_float16_5(Model *model) {
+  OperandType type0(Type::BOOL, {});
+  OperandType type19(Type::TENSOR_INT32, {1});
+  OperandType type30(Type::FLOAT16, {});
+  OperandType type40(Type::TENSOR_FLOAT16, {0, 0, 0, 0});
+  OperandType type5(Type::INT32, {});
+  OperandType type91(Type::TENSOR_FLOAT16, {1, 4});
+  OperandType type96(Type::TENSOR_FLOAT16, {1, 1, 512, 8});
+  // Phase 1, operands
+  auto in5 = model->addOperand(&type96);
+  auto roi5 = model->addOperand(&type91);
+  auto param41 = model->addOperand(&type19);
+  auto param42 = model->addOperand(&type5);
+  auto param43 = model->addOperand(&type5);
+  auto param44 = model->addOperand(&type30);
+  auto param45 = model->addOperand(&type30);
+  auto param46 = model->addOperand(&type5);
+  auto param47 = model->addOperand(&type5);
+  auto layout = model->addOperand(&type0);
+  auto out4 = model->addOperand(&type40);
+  // Phase 2, operations
+  static int32_t param41_init[] = {0};
+  model->setOperandValue(param41, param41_init, sizeof(int32_t) * 1);
+  static int32_t param42_init[] = {128};
+  model->setOperandValue(param42, param42_init, sizeof(int32_t) * 1);
+  static int32_t param43_init[] = {4};
+  model->setOperandValue(param43, param43_init, sizeof(int32_t) * 1);
+  static _Float16 param44_init[] = {1.0f};
+  model->setOperandValue(param44, param44_init, sizeof(_Float16) * 1);
+  static _Float16 param45_init[] = {64.0f};
+  model->setOperandValue(param45, param45_init, sizeof(_Float16) * 1);
+  static int32_t param46_init[] = {10};
+  model->setOperandValue(param46, param46_init, sizeof(int32_t) * 1);
+  static int32_t param47_init[] = {10};
+  model->setOperandValue(param47, param47_init, sizeof(int32_t) * 1);
+  static bool8 layout_init[] = {true};
+  model->setOperandValue(layout, layout_init, sizeof(bool8) * 1);
+  model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in5, roi5, param41, param42, param43, param44, param45, param46, param47, layout}, {out4});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {in5, roi5},
+    {out4});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+

diff --git a/nn/runtime/test/generated/tests/roi_align.mod.py.cpp b/nn/runtime/test/generated/tests/roi_align.mod.py.cpp
index 17aca18..b01f21a 100644
--- a/nn/runtime/test/generated/tests/roi_align.mod.py.cpp
+++ b/nn/runtime/test/generated/tests/roi_align.mod.py.cpp

@@ -489,3 +489,99 @@
             roi_align::get_examples_zero_sized_dynamic_output_shape_nchw_float16());
 }
 
+TEST_F(GeneratedTests, roi_align_nhwc_5) {
+    execute(roi_align::CreateModel_nhwc_5,
+            roi_align::is_ignored_nhwc_5,
+            roi_align::get_examples_nhwc_5());
+}
+
+TEST_F(GeneratedTests, roi_align_nhwc_relaxed_5) {
+    execute(roi_align::CreateModel_nhwc_relaxed_5,
+            roi_align::is_ignored_nhwc_relaxed_5,
+            roi_align::get_examples_nhwc_relaxed_5());
+}
+
+TEST_F(GeneratedTests, roi_align_nhwc_quant8_5) {
+    execute(roi_align::CreateModel_nhwc_quant8_5,
+            roi_align::is_ignored_nhwc_quant8_5,
+            roi_align::get_examples_nhwc_quant8_5());
+}
+
+TEST_F(GeneratedTests, roi_align_nhwc_float16_5) {
+    execute(roi_align::CreateModel_nhwc_float16_5,
+            roi_align::is_ignored_nhwc_float16_5,
+            roi_align::get_examples_nhwc_float16_5());
+}
+
+TEST_F(GeneratedTests, roi_align_nchw_5) {
+    execute(roi_align::CreateModel_nchw_5,
+            roi_align::is_ignored_nchw_5,
+            roi_align::get_examples_nchw_5());
+}
+
+TEST_F(GeneratedTests, roi_align_nchw_relaxed_5) {
+    execute(roi_align::CreateModel_nchw_relaxed_5,
+            roi_align::is_ignored_nchw_relaxed_5,
+            roi_align::get_examples_nchw_relaxed_5());
+}
+
+TEST_F(GeneratedTests, roi_align_nchw_quant8_5) {
+    execute(roi_align::CreateModel_nchw_quant8_5,
+            roi_align::is_ignored_nchw_quant8_5,
+            roi_align::get_examples_nchw_quant8_5());
+}
+
+TEST_F(GeneratedTests, roi_align_nchw_float16_5) {
+    execute(roi_align::CreateModel_nchw_float16_5,
+            roi_align::is_ignored_nchw_float16_5,
+            roi_align::get_examples_nchw_float16_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nhwc_5,
+            roi_align::is_ignored_dynamic_output_shape_nhwc_5,
+            roi_align::get_examples_dynamic_output_shape_nhwc_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_relaxed_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nhwc_relaxed_5,
+            roi_align::is_ignored_dynamic_output_shape_nhwc_relaxed_5,
+            roi_align::get_examples_dynamic_output_shape_nhwc_relaxed_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_quant8_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nhwc_quant8_5,
+            roi_align::is_ignored_dynamic_output_shape_nhwc_quant8_5,
+            roi_align::get_examples_dynamic_output_shape_nhwc_quant8_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nhwc_float16_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nhwc_float16_5,
+            roi_align::is_ignored_dynamic_output_shape_nhwc_float16_5,
+            roi_align::get_examples_dynamic_output_shape_nhwc_float16_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nchw_5,
+            roi_align::is_ignored_dynamic_output_shape_nchw_5,
+            roi_align::get_examples_dynamic_output_shape_nchw_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_relaxed_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nchw_relaxed_5,
+            roi_align::is_ignored_dynamic_output_shape_nchw_relaxed_5,
+            roi_align::get_examples_dynamic_output_shape_nchw_relaxed_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_quant8_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nchw_quant8_5,
+            roi_align::is_ignored_dynamic_output_shape_nchw_quant8_5,
+            roi_align::get_examples_dynamic_output_shape_nchw_quant8_5());
+}
+
+TEST_F(DynamicOutputShapeTest, roi_align_dynamic_output_shape_nchw_float16_5) {
+    execute(roi_align::CreateModel_dynamic_output_shape_nchw_float16_5,
+            roi_align::is_ignored_dynamic_output_shape_nchw_float16_5,
+            roi_align::get_examples_dynamic_output_shape_nchw_float16_5());
+}
+

diff --git a/nn/runtime/test/generated/vts_models/roi_align.model.cpp b/nn/runtime/test/generated/vts_models/roi_align.model.cpp
index 3e8f75e..f1354b6 100644
--- a/nn/runtime/test/generated/vts_models/roi_align.model.cpp
+++ b/nn/runtime/test/generated/vts_models/roi_align.model.cpp

@@ -12404,3 +12404,2151 @@
   return ignore.find(i) != ignore.end();
 }
 
+// Create the model
+Model createTestModel_nhwc_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 128, 4, 1},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_nhwc_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_nhwc_relaxed_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 128, 4, 1},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+        .relaxComputationFloat32toFloat16 = true,
+    };
+}
+
+inline bool is_ignored_nhwc_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_nhwc_quant8_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.25f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT16_ASYMM,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.125f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {1, 128, 4, 1},
+            .numberOfConsumers = 0,
+            .scale = 0.0625f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_nhwc_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_nhwc_float16_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 2},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 14, .length = 2},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 128, 4, 1},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 60, 0, 84, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_nhwc_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_nchw_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 1, 128, 4},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_nchw_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_nchw_relaxed_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 1, 128, 4},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+        .relaxComputationFloat32toFloat16 = true,
+    };
+}
+
+inline bool is_ignored_nchw_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_nchw_quant8_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.25f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT16_ASYMM,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.125f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {1, 1, 128, 4},
+            .numberOfConsumers = 0,
+            .scale = 0.0625f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_nchw_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_nchw_float16_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 2},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 14, .length = 2},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 1, 128, 4},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 60, 0, 84, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_nchw_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nhwc_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nhwc_relaxed_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+        .relaxComputationFloat32toFloat16 = true,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nhwc_quant8_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.25f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT16_ASYMM,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.125f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0625f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nhwc_float16_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 512, 8, 1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 2},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 14, .length = 2},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 60, 0, 84, 10, 0, 0, 0, 10, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nhwc_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nchw_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nchw_relaxed_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+        .relaxComputationFloat32toFloat16 = true,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_relaxed_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nchw_quant8_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.25f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT16_ASYMM,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.125f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 28, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_QUANT8_ASYMM,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0625f,
+            .zeroPoint = 128,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 0, 128, 63, 0, 0, 128, 66, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_quant8_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape_nchw_float16_5() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 1, 512, 8},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {1, 4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_INT32,
+            .dimensions = {1},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 8, .length = 4},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 12, .length = 2},
+        },
+        {
+            .type = OperandType::FLOAT16,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 14, .length = 2},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 16, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 20, .length = 4},
+        },
+        {
+            .type = OperandType::BOOL,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 24, .length = 1},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {0, 0, 0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::ROI_ALIGN,
+            .inputs = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9},
+            .outputs = {10},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1};
+    const std::vector<uint32_t> outputIndexes = {10};
+    std::vector<uint8_t> operandValues = {
+      0, 0, 0, 0, 128, 0, 0, 0, 4, 0, 0, 0, 0, 60, 0, 84, 10, 0, 0, 0, 10, 0, 0, 0, 1
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape_nchw_float16_5(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
+

diff --git a/nn/runtime/test/specs/V1_2/roi_align.mod.py b/nn/runtime/test/specs/V1_2/roi_align.mod.py
index e6bdee6..d1b1303 100644
--- a/nn/runtime/test/specs/V1_2/roi_align.mod.py
+++ b/nn/runtime/test/specs/V1_2/roi_align.mod.py

@@ -243,3 +243,23 @@
     o2: [0],
     zero_sized: [0],
 }).AddNchw(i1, zero_sized, layout).AddVariations("relaxed", quant8, "float16")
+
+
+# TEST 6: ROI_ALIGN_6, hanging issue
+i4 = Input("in", "TENSOR_FLOAT32", "{1, 512, 8, 1}")
+roi4 = Input("roi", "TENSOR_FLOAT32", "{1, 4}")
+o4 = Output("out", "TENSOR_FLOAT32", "{1, 128, 4, 1}")
+Model().Operation("ROI_ALIGN", i4, roi4, [0], 128, 4, 1.0, 64.0, 10, 10, layout).To(o4)
+
+quant8 = DataTypeConverter().Identify({
+    i4: ("TENSOR_QUANT8_ASYMM", 0.25, 128),
+    roi4: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+    o4: ("TENSOR_QUANT8_ASYMM", 0.0625, 128)
+})
+
+# Instantiate an example
+Example({
+    i4: [0] * (512 * 8),
+    roi4: [450, 500, 466, 508],
+    o4: [0] * (128 * 4)
+}).AddNchw(i4, o4, layout).AddVariations("relaxed", quant8, "float16")
commit	dbd612b13b93039b106a414c74473c621d68d8fc	[log] [tgz]
author	Xusong Wang <xusongw@google.com>	Thu Apr 18 21:58:17 2019 +0000
committer	Android (Google) Code Review <android-gerrit@google.com>	Thu Apr 18 21:58:17 2019 +0000
tree	aeb34fc403850cdf2902b5a636550a1b4ba64dc8
parent	f21abd681dae072080d8b29e48c8c68f13d1f2d1 [diff]
parent	68ff21cdc5ffd4a9d0333a45744623f2456e0480 [diff]