diff --git a/samples/CMakeLists.txt b/samples/CMakeLists.txt
index 375b7aa..cba6031 100644
--- a/samples/CMakeLists.txt
+++ b/samples/CMakeLists.txt
@@ -18,4 +18,5 @@ endif()
 
 if(TIM_VX_ENABLE_PLATFORM)
     add_subdirectory("lenet_multi_device")
+    add_subdirectory("multi_device")
 endif()
diff --git a/samples/multi_device/CMakeLists.txt b/samples/multi_device/CMakeLists.txt
new file mode 100644
index 0000000..a0af36e
--- /dev/null
+++ b/samples/multi_device/CMakeLists.txt
@@ -0,0 +1,14 @@
+message("samples/multi_device")
+
+set(TARGET_NAME "multi_device")
+
+find_package(Threads REQUIRED)
+
+aux_source_directory(. ${TARGET_NAME}_SRCS)
+add_executable(${TARGET_NAME} ${${TARGET_NAME}_SRCS})
+
+target_link_libraries(${TARGET_NAME} PRIVATE tim-vx Threads::Threads)
+target_include_directories(${TARGET_NAME} PRIVATE 
+    ${CMAKE_CURRENT_SOURCE_DIR}
+    ${PROJECT_SOURCE_DIR}/include
+)
diff --git a/samples/multi_device/README b/samples/multi_device/README
new file mode 100644
index 0000000..557e20d
--- /dev/null
+++ b/samples/multi_device/README
@@ -0,0 +1,16 @@
+## brief
+The multi_device demo uses some acuity exported tim-vx networks, and running on 4 devices of NPU using platform api.
+
+## environment
+  export VSIMULATOR_CONFIG=VIP9400O_PID0XD9
+  export VIV_MGPU_AFFINITY="1:0"
+  export VIV_OVX_USE_MULTI_DEVICE="1:1"
+  export TIM_VX_ROOT="${workspaceFolder}/tim-vx"
+
+## build
+cd build
+cmake .. -DCMAKE_BUILD_TYPE=Debug -DTIM_VX_BUILD_EXAMPLES=ON -DTIM_VX_ENABLE_PLATFORM=ON
+
+## run
+cd build
+./samples/multi_device/multi_device
diff --git a/samples/multi_device/lenet/lenet.export.data b/samples/multi_device/lenet/lenet.export.data
new file mode 100644
index 0000000..04278c8
Binary files /dev/null and b/samples/multi_device/lenet/lenet.export.data differ
diff --git a/samples/multi_device/lenet/lenet_input_1_1_28_28_uint8.bin b/samples/multi_device/lenet/lenet_input_1_1_28_28_uint8.bin
new file mode 100644
index 0000000..4f397eb
Binary files /dev/null and b/samples/multi_device/lenet/lenet_input_1_1_28_28_uint8.bin differ
diff --git a/samples/multi_device/mobilenet/mobilenet.export.data b/samples/multi_device/mobilenet/mobilenet.export.data
new file mode 100644
index 0000000..e337dbc
Binary files /dev/null and b/samples/multi_device/mobilenet/mobilenet.export.data differ
diff --git a/samples/multi_device/mobilenet/mobilenet_1_224_224_3_uint8.bin b/samples/multi_device/mobilenet/mobilenet_1_224_224_3_uint8.bin
new file mode 100644
index 0000000..b726a44
--- /dev/null
+++ b/samples/multi_device/mobilenet/mobilenet_1_224_224_3_uint8.bin
@@ -0,0 +1,550 @@
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\ No newline at end of file
diff --git a/samples/multi_device/multi_device.cc b/samples/multi_device/multi_device.cc
new file mode 100644
index 0000000..0e722f3
--- /dev/null
+++ b/samples/multi_device/multi_device.cc
@@ -0,0 +1,187 @@
+/****************************************************************************
+*
+*    Copyright (c) 2020 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
+#include <algorithm>
+#include <iomanip>
+#include <iostream>
+#include <fstream>
+#include <cstring>
+#include <tuple>
+#include <vector>
+#include <assert.h>
+#include <chrono>
+#include <thread>
+
+#include "tim/vx/context.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/platform/platform.h"
+#include "tim/vx/platform/native.h"
+#include "vx_lenet.h"
+#include "vx_mobilenet.h"
+#include "vx_resnet50.h"
+
+template <typename T>
+static void printTopN(const T* prob, int outputCount, int topNum) {
+  std::vector<std::tuple<int, T>> data;
+
+  for (int i = 0; i < outputCount; i++) {
+    data.push_back(std::make_tuple(i, prob[i]));
+  }
+
+  std::sort(data.begin(), data.end(),
+    [](auto& a, auto& b) { return std::get<1>(a) > std::get<1>(b); });
+
+  std::cout << " --- Top" << topNum << " ---" << std::endl;
+  for (int i = 0; i < topNum; i++) {
+    std::cout << std::setw(3) << std::get<0>(data[i]) << ": " << std::fixed
+              << std::setprecision(6) << std::get<1>(data[i]) << std::endl;
+  }
+}
+
+template <typename T>
+void print_topN(std::size_t size, std::shared_ptr<tim::vx::platform::ITensorHandle> handle) {
+  std::vector<T> output_data;
+  output_data.resize(size);
+  if (!handle->CopyDataFromTensor(output_data.data())) {
+    std::cout << "Copy output data fail." << std::endl;
+  }
+  printTopN(output_data.data(), output_data.size(), 5);
+}
+
+std::vector<std::vector<char>> load_input_data(std::vector<std::string> filenames, std::vector<uint32_t> input_size_bytes) {
+  std::vector<std::vector<char>> Data;
+  for (std::size_t i = 0; i < filenames.size(); i++) {
+    std::ifstream fin(filenames[i], std::ios::in | std::ios::binary);
+    if (fin) {
+        std::vector<char> input_data;
+        fin.seekg(0, std::ios::end);
+        int size = fin.tellg();
+        fin.seekg(0, std::ios::beg);
+        char *buffer = new char[size];
+        std::cout<<"File "<<filenames[i] <<" size:"<<size<<std::endl;
+        fin.read(buffer, size);
+        fin.close();
+        input_data.assign(buffer, buffer + input_size_bytes[i]);
+        Data.push_back(input_data);
+        free(buffer);
+    }
+   }
+    return Data;
+}
+
+void executor_trigger(std::shared_ptr<tim::vx::platform::IExecutor> executor) {
+  executor->Trigger();
+}
+
+auto context = tim::vx::Context::Create();
+std::pair<std::shared_ptr<tim::vx::platform::IExecutable>, std::shared_ptr<tim::vx::platform::ITensorHandle>> generate_executable(
+    std::shared_ptr<tim::vx::platform::IExecutor> executor,
+    std::function<void(std::shared_ptr<tim::vx::Graph>, const char*)> construct_func,
+    std::string weight_file,
+    std::vector<std::string> input_files, tim::vx::ShapeType input_size_bytes) {
+  auto graph = context->CreateGraph();
+  const char* weight_file_c = weight_file.c_str();
+  construct_func(graph, weight_file_c);
+  auto input_data = load_input_data(input_files, input_size_bytes);
+  auto executable = tim::vx::platform::Compile(graph, executor);  // compile to nbg
+  auto input_handle = executable->AllocateTensor(graph->InputsTensor()[0]->GetSpec());
+  auto output_handle = executable->AllocateTensor(graph->OutputsTensor()[0]->GetSpec());
+  executable->SetInput(input_handle);
+  executable->SetOutput(output_handle);
+  input_handle->CopyDataToTensor(input_data[0].data(), input_data[0].size());
+  return std::make_pair(executable, output_handle);
+}
+
+int main(int argc, char** argv) {
+  (void) argc, (void) argv;
+  auto devices = tim::vx::platform::NativeDevice::Enumerate();
+  auto device0 = devices[0];
+  std::shared_ptr<tim::vx::platform::IExecutor> executor0 = std::make_shared<tim::vx::platform::NativeExecutor> (device0);
+  auto device1 = devices[1];
+  std::shared_ptr<tim::vx::platform::IExecutor> executor1 = std::make_shared<tim::vx::platform::NativeExecutor> (device1);
+  auto device2 = devices[2];
+  std::shared_ptr<tim::vx::platform::IExecutor> executor2 = std::make_shared<tim::vx::platform::NativeExecutor> (device2);
+  auto device3 = devices[3];
+  std::shared_ptr<tim::vx::platform::IExecutor> executor3 = std::make_shared<tim::vx::platform::NativeExecutor> (device3);
+
+  auto root = std::getenv("TIM_VX_ROOT");
+  assert(root != NULL);
+  std::string ROOT(root);
+  std::vector<std::string> lenet_input_files = {ROOT + "/samples/multi_device/lenet/lenet_input_1_1_28_28_uint8.bin"};
+  auto lenet_input_bytes = acuitylite::lenet::input_bytes_list;
+  auto lenet_weight_file = ROOT + "/samples/multi_device/lenet/lenet.export.data";
+  std::function<void(std::shared_ptr<tim::vx::Graph>, const char*)> lenet_construct_func = acuitylite::lenet::construct_graph;
+
+  std::vector<std::string> mobilenet_input_files = {ROOT + "/samples/multi_device/mobilenet/mobilenet_1_224_224_3_uint8.bin"};
+  auto mobilenet_input_bytes = acuitylite::mobilenet::input_bytes_list;
+  auto mobilenet_weight_file = ROOT + "/samples/multi_device/mobilenet/mobilenet.export.data";
+  std::function<void(std::shared_ptr<tim::vx::Graph>, const char*)> mobilenet_construct_func = acuitylite::mobilenet::construct_graph;
+
+  std::vector<std::string> resnet50_input_files = {ROOT + "/samples/multi_device/resnet50/resnet50_1_3_224_224_uint8.bin"};
+  auto resnet50_input_bytes = acuitylite::resnet50::input_bytes_list;
+  auto resnet50_weight_file = ROOT + "/samples/multi_device/resnet50/resnet50.export.data";
+  std::function<void(std::shared_ptr<tim::vx::Graph>, const char*)> resnet50_construct_func = acuitylite::resnet50::construct_graph;
+
+  std::shared_ptr<tim::vx::platform::IExecutable> lenet_0, lenet_2, lenet_3, mobilenet_1, mobilenet_2, mobilenet_3, resnet50_0, resnet50_1;
+  std::shared_ptr<tim::vx::platform::ITensorHandle> lenet_0_outhandle, lenet_2_outhandle, lenet_3_outhandle, mobilenet_1_outhandle, mobilenet_2_outhandle, mobilenet_3_outhandle,
+    resnet50_0_outhandle, resnet50_1_outhandle;
+
+  std::tie(lenet_0, lenet_0_outhandle) = generate_executable(executor0, lenet_construct_func, lenet_weight_file, lenet_input_files, lenet_input_bytes);
+  std::tie(resnet50_0, resnet50_0_outhandle) = generate_executable(executor0, resnet50_construct_func, resnet50_weight_file, resnet50_input_files, resnet50_input_bytes);
+  executor0->Submit(lenet_0, lenet_0);
+  executor0->Submit(resnet50_0, lenet_0);
+
+  std::tie(mobilenet_1, mobilenet_1_outhandle) = generate_executable(executor1, mobilenet_construct_func, mobilenet_weight_file, mobilenet_input_files, mobilenet_input_bytes);
+  std::tie(resnet50_1, resnet50_1_outhandle) = generate_executable(executor1, resnet50_construct_func, resnet50_weight_file, resnet50_input_files, resnet50_input_bytes);
+  auto executable_set1 = tim::vx::platform::CreateExecutableSet({mobilenet_1, resnet50_1});
+  executor1->Submit(executable_set1, executable_set1);
+
+  std::tie(lenet_2, lenet_2_outhandle) = generate_executable(executor2, lenet_construct_func, lenet_weight_file, lenet_input_files, lenet_input_bytes);
+  std::tie(mobilenet_2, mobilenet_2_outhandle) = generate_executable(executor2, mobilenet_construct_func, mobilenet_weight_file, mobilenet_input_files, mobilenet_input_bytes);
+  auto executable_set2 = tim::vx::platform::CreateExecutableSet({lenet_2, mobilenet_2});
+  executor2->Submit(executable_set2, executable_set2);
+
+  std::tie(lenet_3, lenet_3_outhandle) = generate_executable(executor3, lenet_construct_func, lenet_weight_file, lenet_input_files, lenet_input_bytes);
+  std::tie(mobilenet_3, mobilenet_3_outhandle) = generate_executable(executor3, mobilenet_construct_func, mobilenet_weight_file, mobilenet_input_files, mobilenet_input_bytes);
+  auto executable_set3 = tim::vx::platform::CreateExecutableSet({lenet_3, mobilenet_3});
+  executor3->Submit(executable_set3, executable_set3);
+
+  std::thread t0(executor_trigger, executor0);
+  std::thread t1(executor_trigger, executor1);
+  std::thread t2(executor_trigger, executor2);
+  std::thread t3(executor_trigger, executor3);
+  t0.join();
+  t1.join();
+  t2.join();
+  t3.join();
+
+  print_topN<float>(1 * 10, lenet_0_outhandle);
+  print_topN<float>(1 * 10, lenet_2_outhandle);
+  print_topN<float>(1 * 10, lenet_3_outhandle);
+  print_topN<float>(1 * 1001, mobilenet_1_outhandle);
+  print_topN<float>(1 * 1001, mobilenet_2_outhandle);
+  print_topN<float>(1 * 1001, mobilenet_3_outhandle);
+  print_topN<uint16_t>(1 * 1000, resnet50_0_outhandle);
+  print_topN<uint16_t>(1 * 1000, resnet50_1_outhandle);
+  return 0;
+}
diff --git a/samples/multi_device/multi_device_demo.cc b/samples/multi_device/multi_device_demo.cc
index fd5bdca..7aebab2 100644
--- a/samples/multi_device/multi_device_demo.cc
+++ b/samples/multi_device/multi_device_demo.cc
@@ -1,3 +1,26 @@
+/****************************************************************************
+*
+*    Copyright (c) 2020 Vivante Corporation
+*
+*    Permission is hereby granted, free of charge, to any person obtaining a
+*    copy of this software and associated documentation files (the "Software"),
+*    to deal in the Software without restriction, including without limitation
+*    the rights to use, copy, modify, merge, publish, distribute, sublicense,
+*    and/or sell copies of the Software, and to permit persons to whom the
+*    Software is furnished to do so, subject to the following conditions:
+*
+*    The above copyright notice and this permission notice shall be included in
+*    all copies or substantial portions of the Software.
+*
+*    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+*    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+*    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+*    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+*    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+*    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+*    DEALINGS IN THE SOFTWARE.
+*
+*****************************************************************************/
 #include <algorithm>
 #include <iostream>
 #include <vector>
@@ -11,7 +34,7 @@
 static void printTopN() {
 }
 
-int main(int argc, char** argv) {
+int demo(int argc, char** argv) {
   (void) argc, (void) argv;
   std::vector<uint8_t> input_data = {};
   auto context = tim::vx::Context::Create();
diff --git a/samples/multi_device/resnet50/resnet50.export.data b/samples/multi_device/resnet50/resnet50.export.data
new file mode 100644
index 0000000..d79ff42
Binary files /dev/null and b/samples/multi_device/resnet50/resnet50.export.data differ
diff --git a/samples/multi_device/resnet50/resnet50_1_3_224_224_uint8.bin b/samples/multi_device/resnet50/resnet50_1_3_224_224_uint8.bin
new file mode 100644
index 0000000..099cf6f
--- /dev/null
+++ b/samples/multi_device/resnet50/resnet50_1_3_224_224_uint8.bin
@@ -0,0 +1,550 @@
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eb^Y[]YUT[ZZTST`_MOSJRXaWi]PQLBFDCGC>;?D=::;=<8<77967=><BCCD?=@?9;93693861:.2;-,389;GD9CIHFFLIQQVRSTZbfgknkhkwvtvxrrvxwy}‚ƒƒŠ‡�€�‡ƒ…„ƒƒ†…ˆ��…‰Šˆ…†„ˆ—Ž…‰–œ•šŸ�”�‘ž�’�žš„ˆŽ•ŽŒ„‡’‘…~€�€qrvlglovvqlormcdbegdbgojjlihaUR[XWVVZ^^XYa^`XZ[+/!*7169=8LD6<@<?<B<B9?=AAB@BCB<;8=A<=BADD@>@?;;<;<@GLKGDA9>DDBAGOS`\VZc_dnrmimtwejxmuv{w{||}||�ˆ‰‚ƒ€€‰„�ƒ…„~‚††ƒ……‰……„††�‡�ŽŽ‰‡�Š†~‡ˆ��‰…ŠŽŽšŒ„���“�–Ž’£¢¤¨�› Ž�”�–—”…„Ž‡‰ˆ�‡€„ywxl`^__bb_\]ac]ce_ZYVPVXPJX^UWNIKUZXUTVT[Y]TY[::*1?;<;5851/4555365:8<=>FKIEBDDDGQSJKRRXVROQRNJQVSXfkgjffgakvxxvƒ‚‚{~�ƒ{{ƒ}}‚{{z€†|…~{�„„ƒƒƒƒ…Š”‘…‡ŽŠ‘�Š‰…‚…ŒŒŠŠ�‹‰‘š–��‘‘“–””™ž˜„Š™�““™Œ‰�œ¡¡™Ÿ—¡ —•™ §¡š­ ��“ ›“—––‹�—™—‚~y‚€yrmhZQQOKKR[[QHMR\`]UQQJFMQRUX]ZWUWUPOTXTUX[SVZ*.'(,(*-394=?BB>EHPSUU[`bhlmlklqoptrlmomqssqqttqv}xw‚ƒ€ˆ†‡Š�‚‡„…ƒ‰ˆ†ˆŒ‡‚„‚‰€‚Šƒ‰��††Š‚‡‹ŒŠ‰�““’›™•’–š”œš“”˜›š›–š˜ž™–›¦¤š— œ™œš�¡ž�– Ÿ”ž’ �— �› ž›œŸ••££ž ¨œ£˜Œ•šœ�“•Œ�“�‘‹…z}rtom`W[XV[^bcec^YTVY`_UIKTVVaieXS\\b_UNOV^]ZW[ZSUV6:=DMQQR\bgngdojjmxrtluy|zw{€~|ˆ…‚��‚z€~{y|ƒ‡€ƒ„…ˆˆ‰Œˆ�ŒŠ‡‰†ˆ‹�—’““���”“ŠŠ’•›�¥ž›�œ› ��™—›�›˜™˜��–“� ¦žŸž›œ£¡Ÿ¢¥¢¥¦¨©¦Ÿ¡¡¡£ š‘’›£¡’“›¢ ¢¤ ŸœŸŸ™�ŽŽ¤¨‘£›–—š œŒŸž–›•™š—…†‚xpudb^[QJQW`dqeZXXXY[a][ZZVVUONPRUMFPKPNILQOLQSKNEDDEeeknsvrqx{x{†}{v{{ywuuuv‚~{„}„ƒ„…ˆŒŒŠ‰‰…‡†„�…ˆŒ‰‹‹„~zszŠ‰�ˆŒ�“�Ž›•›š’’��š˜Ÿ¥§žŸž ¥¥¢¤”ž¡¢Ÿ£¤£ Ÿ¢§¡œ£œš’œ¤žœ¢˜{�•—’Ÿ§¡œŸ¥¦£˜’™–“Ÿž™‹‹ŸœŒ��«§œ’§§‘›™–�~ƒ Žzšˆ|•‘¢”“›‡…~{|€…vqwfgba`]^eklfSKSUMJNVKDCHGHKIDLLJQUNNOIEJIGINTJJ@FHJp{tq}�‚}�€�wt|{w|~�|u|�„„�~~~‡ƒ†Š„}}{z{ƒ…„ƒ†……ƒ‡ˆ‚~|{‰…†‡�‚†€…Ž�”�Œ�š‹——Ÿ™•�˜Ÿ¬ª¡¡—’™ž—œ¤š£¦§ž¨££¥››™˜¢œš¡ž’‹�ŒŒ—œž¢¨­š£ª �™›£ž§Ÿž˜�•¢¢�„Œ”˜¢Ÿ�‰Œ‹z{dlw’�š“|y‚‚�{…�•”ƒtb[\_frmouyyn^E>BIJLCEG@GIC@CHIMH==9FCDFB=ACAADJFCKFJLxpv~pjz|ƒusznsyw|�„{rxuoz‚€|{{z‚€„‡…€|z|‚€€‚ƒ‡€�…€…�ƒ{‚€�ˆ�†‡Š”—‰�š“’‘�›�Ž’’¢†–•~€›š�ž£™ ¤�  Ÿ œ•˜¢¦©Ÿ£¤ ›–”™�£¥¢œž¥¯¥¤Ÿ™ž›Ÿª¬’zƒ”‹¡®°«§�‚�„Ž�›�ˆz§ª‹…““£ª“Ž�luwt�šŠŽ‘zgfjxŠcTYbi`JNXV_[NVTYJGFGKG?HRVK?@;33;ABC?:8AGHGMEDFlijnlmwwy€wrw|vouxqstw�v|}zsw€††�‹‡…„‚€�}��}{€ƒ�{}„ˆƒ€†„„€rvƒ}~‚Š“˜“�‡•�¤�›ŸŒ™—�€‹œ£˜¡£ œž˜’’£ž˜œ“ƒt ¡“–˜›£˜‘¢¤‹š§—’£±›–”�Š–¡§ž‘Ž‹–¢”Š‘‘{}˜�£—¦¦�¯³†¥œ¡’§§Ž�”‚}szo‚…llrzw|oj`ggVP]_^k`^e_\PIGLHGA=EJIB=CD=DCJH@9668;9<BD;ITSLqsjbhrncjtogzpniiiZ[deq|‚}wty~~zwƒ…†ƒ~}���‚„„yvz†„}€ƒŽ�†ƒƒƒ�Š“‰’• Ÿ�ˆ¥š–¡”‹”���­¨žŽ~‰‚�—”�°±›••™ŠŽª§�‹“ŸŸ”…{v�‘‡€‘›››“xolgaYSWh}Š€~z—„xbj� ´­©¥«Ž��¡‘”�’�~�–Ž€^Pjk[X^gu…{gie`YbrlWTf]T_PJKDPKIH<5;=:CABBBRTPRKID:687>BH?AB@D]MU^Ycme_]bfrhqmajiekopvvv{}{too{ƒ�|yurtqs{�€xtu€w{„|}p}qq�{…’’�“”�†ˆ�š ¦Ž�›‚„�›•€|“›–šš¬Ÿ •”œ“¥›�’}r…“�’�”}|Ž’|�ŠŽ–“�‘�ƒƒˆ‡|}‚…—š—–¤›¦•�’””†‡ˆœ‘~z�‘¢·¬Ÿ›¢š—š�}z}rƒ‰„†‚‚†�‚†qiea]adbd`VWXVSKFB@98?@=>BB?BEH=?56>DGE>?JP>>GF>_FOYHERRQ_fbVYcfdde\Ycgejjosqloxy€‚}zz|~€yw{}{wvyxx~‚‚|pno|{‡„~|„‘�•‹Žœ””¦®ž‘‡˜˜–� �–�–��Œ˜�©¥ ž�†…–•Š”š��‹smyqmmpz‹Œ†”–”Š�Šƒ‰Œ…xˆ��—ŽŠ•™¤�Ÿ©¤š•�‚š¬Œ‘k„w}~‘…˜ž¤›‘›€wx€‰‡|~{mlofZ\]UQ\eZ_TILDEDG=;?@BIJGDC7:3<99;<=@A0.23>DD?YOOQQOMUXNBCBMGCTQWb]RSYWUX[]]bjuspjhmrsy}}shhqy~„{|„|uywzŠ}†„z|ˆ|ˆ“„„–œzŠŽ˜›”«¤ª�¦ž™–—ˆŽ€…››��„�‘“š–‰ˆ™ŸŽ�š�z|‚�€zy‚Œ��„|�‘~yvu„›‚jnrkŠ�†˜œhDcƒq�‘’™¨”aŠ™ž˜ƒŽ¢¤¦Ÿ›•�h…€{€�„…i>BKLQYTCLIHCACBCABC;;BDD@ED>CB?<HGEGKG?:2=FFIJJ?UTNHOLAHG5=A:DD4ENFQejmhliiggflpmjmqpqohcd`]gtummhj}‡|x{{‚€}|t�Š�”†Žœ‘˜¬›”˜Šˆ†p˜‰šŒ™ž”Š‚x‡�‹„Ž�”˜““¡¦˜‡o\`p†—‹ˆ�ˆ‡�Œ‚{|ynfeXQs‹‡i^x‡ˆ�™��„—”y„�–±�x‡¦¤ª¡‘®˜”Ž–¢›Ž—h{Š|}~s~|jpjwsijlVCMLJPDA??@8@B86?EJQF:8>@9ENOICB?=?:=BFHCE=CFMOLG?>?<>767?=D@BKRNYPWTUZ_^cmfivˆyic[QQ[fqro~mr{x~‚wxifk„Š~ŽŠ�‚ŠŠ‘Ÿ–�}ˆ�ˆ€s€�œžŽˆ�““o{‰Šˆ}|zlr~‚Š‚‚„”Œ‡†zƒ š†ks†u}’š�šŽ�ˆpg}•‘‰Œ…xsr}‰‹rl~ˆ¥¯˜{‡««‘{’¤~’–—Ž–£ˆ{‰‡‡‰…vh_abfbad\k‡†tbT?57AB;518?=@PUD23@CEE>BB95203 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\ No newline at end of file
diff --git a/samples/multi_device/vx_lenet.cc b/samples/multi_device/vx_lenet.cc
new file mode 100644
index 0000000..853a1b1
--- /dev/null
+++ b/samples/multi_device/vx_lenet.cc
@@ -0,0 +1,235 @@
+/****************************************************************************
+*   Generated by ACUITY 6.6.0
+*   Match timvx 1.1.30
+*
+*   Neural Network appliction network definition source file
+****************************************************************************/
+#include "vx_lenet.h"
+
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+namespace
+{
+
+char *get_const_data(const char *data_file_name)
+{
+    std::ifstream fin(data_file_name, std::ios::in | std::ios::binary);
+    if (fin)
+    {
+        fin.seekg(0, std::ios::end);
+        int size = fin.tellg();
+        fin.seekg(0, std::ios::beg);
+        char *buffer = new char [size];
+        std::cout<<"File "<<data_file_name <<" size:"<<size<<std::endl;
+        fin.read(buffer, size);
+        fin.close();
+        return buffer;
+    }
+    else
+    {
+        std::cout<<"Load file "<<data_file_name <<" failed"<<std::endl;
+        return NULL;
+    }
+}
+
+}  // namespace
+
+namespace acuitylite
+{
+
+std::vector<std::vector<uint32_t>> lenet::input_size_list = {{28 , 28 , 1 , 1}};
+std::vector<uint32_t> lenet::input_bytes_list = {28 * 28 * 1 * 1 * sizeof(input_0_type)};
+std::vector<std::vector<uint32_t>> lenet::output_size_list = {{10 , 1}};
+std::vector<std::shared_ptr<tim::vx::Tensor>> lenet::inputs_tensor;
+std::vector<std::shared_ptr<tim::vx::Tensor>> lenet::outputs_tensor;
+
+void lenet::construct_graph
+    (
+    std::shared_ptr<tim::vx::Graph> graph,
+    const char *data_file_name
+    )
+{
+    char *coef_data_ptr = get_const_data(data_file_name);
+
+    tim::vx::Quantization convolution_1_out0_quant(tim::vx::QuantType::ASYMMETRIC, 5.209146976470947, 131);
+    tim::vx::TensorSpec convolution_1_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_1_out0_quant);
+    auto convolution_1_out0 = graph->CreateTensor(convolution_1_out0_spec);
+
+    tim::vx::ShapeType convolution_1_weight_shape({5,5,1,20});
+    tim::vx::Quantization convolution_1_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0033623368944972754, 119);
+    tim::vx::TensorSpec convolution_1_weight_spec(tim::vx::DataType::UINT8, convolution_1_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_1_weight_quant);
+    auto convolution_1_weight = graph->CreateTensor(convolution_1_weight_spec, coef_data_ptr + 80);
+
+    tim::vx::ShapeType convolution_1_bias_shape({20});
+    tim::vx::Quantization convolution_1_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0033623368944972754, 0);
+    tim::vx::TensorSpec convolution_1_bias_spec(tim::vx::DataType::INT32, convolution_1_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_1_bias_quant);
+    auto convolution_1_bias = graph->CreateTensor(convolution_1_bias_spec, coef_data_ptr + 0);
+
+    tim::vx::Quantization pooling_2_out0_quant(tim::vx::QuantType::ASYMMETRIC, 5.209146976470947, 131);
+    tim::vx::TensorSpec pooling_2_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, pooling_2_out0_quant);
+    auto pooling_2_out0 = graph->CreateTensor(pooling_2_out0_spec);
+
+    tim::vx::Quantization convolution_3_out0_quant(tim::vx::QuantType::ASYMMETRIC, 10.594023704528809, 145);
+    tim::vx::TensorSpec convolution_3_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_3_out0_quant);
+    auto convolution_3_out0 = graph->CreateTensor(convolution_3_out0_spec);
+
+    tim::vx::ShapeType convolution_3_weight_shape({5,5,20,50});
+    tim::vx::Quantization convolution_3_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0011482049012556672, 128);
+    tim::vx::TensorSpec convolution_3_weight_spec(tim::vx::DataType::UINT8, convolution_3_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_3_weight_quant);
+    auto convolution_3_weight = graph->CreateTensor(convolution_3_weight_spec, coef_data_ptr + 780);
+
+    tim::vx::ShapeType convolution_3_bias_shape({50});
+    tim::vx::Quantization convolution_3_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.005981168244034052, 0);
+    tim::vx::TensorSpec convolution_3_bias_spec(tim::vx::DataType::INT32, convolution_3_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_3_bias_quant);
+    auto convolution_3_bias = graph->CreateTensor(convolution_3_bias_spec, coef_data_ptr + 580);
+
+    tim::vx::Quantization pooling_4_out0_quant(tim::vx::QuantType::ASYMMETRIC, 10.594023704528809, 145);
+    tim::vx::TensorSpec pooling_4_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, pooling_4_out0_quant);
+    auto pooling_4_out0 = graph->CreateTensor(pooling_4_out0_spec);
+
+    tim::vx::Quantization fullconnect_5_out0_quant(tim::vx::QuantType::ASYMMETRIC, 4.961546421051025, 0);
+    tim::vx::TensorSpec fullconnect_5_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, fullconnect_5_out0_quant);
+    auto fullconnect_5_out0 = graph->CreateTensor(fullconnect_5_out0_spec);
+
+    tim::vx::ShapeType fullconnect_5_weight_shape({800,500});
+    tim::vx::Quantization fullconnect_5_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0007354848785325885, 130);
+    tim::vx::TensorSpec fullconnect_5_weight_spec(tim::vx::DataType::UINT8, fullconnect_5_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, fullconnect_5_weight_quant);
+    auto fullconnect_5_weight = graph->CreateTensor(fullconnect_5_weight_spec, coef_data_ptr + 27780);
+
+    tim::vx::ShapeType fullconnect_5_bias_shape({500});
+    tim::vx::Quantization fullconnect_5_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.007791744079440832, 0);
+    tim::vx::TensorSpec fullconnect_5_bias_spec(tim::vx::DataType::INT32, fullconnect_5_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, fullconnect_5_bias_quant);
+    auto fullconnect_5_bias = graph->CreateTensor(fullconnect_5_bias_spec, coef_data_ptr + 25780);
+
+    tim::vx::Quantization relu_6_out0_quant(tim::vx::QuantType::ASYMMETRIC, 4.961546421051025, 0);
+    tim::vx::TensorSpec relu_6_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_6_out0_quant);
+    auto relu_6_out0 = graph->CreateTensor(relu_6_out0_spec);
+
+    tim::vx::Quantization fullconnect_7_out0_quant(tim::vx::QuantType::ASYMMETRIC, 16.404624938964844, 81);
+    tim::vx::TensorSpec fullconnect_7_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, fullconnect_7_out0_quant);
+    auto fullconnect_7_out0 = graph->CreateTensor(fullconnect_7_out0_spec);
+
+    tim::vx::ShapeType fullconnect_7_weight_shape({500,10});
+    tim::vx::Quantization fullconnect_7_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0015804264694452286, 135);
+    tim::vx::TensorSpec fullconnect_7_weight_spec(tim::vx::DataType::UINT8, fullconnect_7_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, fullconnect_7_weight_quant);
+    auto fullconnect_7_weight = graph->CreateTensor(fullconnect_7_weight_spec, coef_data_ptr + 427820);
+
+    tim::vx::ShapeType fullconnect_7_bias_shape({10});
+    tim::vx::Quantization fullconnect_7_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00784135889261961, 0);
+    tim::vx::TensorSpec fullconnect_7_bias_spec(tim::vx::DataType::INT32, fullconnect_7_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, fullconnect_7_bias_quant);
+    auto fullconnect_7_bias = graph->CreateTensor(fullconnect_7_bias_spec, coef_data_ptr + 427780);
+
+    tim::vx::ShapeType input_0_shape({28,28,1,1});
+    tim::vx::Quantization input_0_quant(tim::vx::QuantType::ASYMMETRIC, 1.0, 0);
+    tim::vx::TensorSpec input_0_spec(tim::vx::DataType::UINT8, input_0_shape,
+    tim::vx::TensorAttribute::INPUT, input_0_quant);
+    auto input_0 = graph->CreateTensor(input_0_spec);
+
+    tim::vx::ShapeType output_9_shape({10,1});
+    tim::vx::TensorSpec output_9_spec(tim::vx::DataType::FLOAT32, output_9_shape,
+    tim::vx::TensorAttribute::OUTPUT);
+    auto output_9 = graph->CreateTensor(output_9_spec);
+
+    lenet::inputs_tensor.push_back(input_0);
+
+    lenet::outputs_tensor.push_back(output_9);
+
+    auto convolution_1 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        20,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({5,5}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto pooling_2 = graph->CreateOperation <tim::vx::ops::Pool2d>(
+        tim::vx::PoolType::MAX,    // type
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        std::array<uint32_t, 2>({2,2}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        tim::vx::RoundType::CEILING);    // round_type
+
+    auto convolution_3 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        50,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({5,5}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto pooling_4 = graph->CreateOperation <tim::vx::ops::Pool2d>(
+        tim::vx::PoolType::MAX,    // type
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        std::array<uint32_t, 2>({2,2}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        tim::vx::RoundType::CEILING);    // round_type
+
+    auto fullconnect_5 = graph->CreateOperation <tim::vx::ops::FullyConnected>(
+        2,    // axis
+        500);    // weights
+
+    auto relu_6 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto fullconnect_7 = graph->CreateOperation <tim::vx::ops::FullyConnected>(
+        0,    // axis
+        10);    // weights
+
+    auto softmax_8 = graph->CreateOperation <tim::vx::ops::Softmax>(
+        1.0,    // beta
+        0);    // axis
+
+    (*convolution_1)
+        .BindInputs({input_0, convolution_1_weight, convolution_1_bias})
+        .BindOutputs({convolution_1_out0});
+
+    (*pooling_2)
+        .BindInputs({convolution_1_out0})
+        .BindOutputs({pooling_2_out0});
+
+    (*convolution_3)
+        .BindInputs({pooling_2_out0, convolution_3_weight, convolution_3_bias})
+        .BindOutputs({convolution_3_out0});
+
+    (*pooling_4)
+        .BindInputs({convolution_3_out0})
+        .BindOutputs({pooling_4_out0});
+
+    (*fullconnect_5)
+        .BindInputs({pooling_4_out0, fullconnect_5_weight, fullconnect_5_bias})
+        .BindOutputs({fullconnect_5_out0});
+
+    (*relu_6)
+        .BindInputs({fullconnect_5_out0})
+        .BindOutputs({relu_6_out0});
+
+    (*fullconnect_7)
+        .BindInputs({relu_6_out0, fullconnect_7_weight, fullconnect_7_bias})
+        .BindOutputs({fullconnect_7_out0});
+
+    (*softmax_8)
+        .BindInputs({fullconnect_7_out0})
+        .BindOutputs({output_9});
+
+    free(coef_data_ptr);
+}
+
+}  // namespace acuitylite
diff --git a/samples/multi_device/vx_lenet.h b/samples/multi_device/vx_lenet.h
new file mode 100644
index 0000000..94406f6
--- /dev/null
+++ b/samples/multi_device/vx_lenet.h
@@ -0,0 +1,34 @@
+/****************************************************************************
+*   Generated by ACUITY 6.6.0
+*   Match timvx 1.1.30
+*
+*   Neural Network appliction network definition header file
+****************************************************************************/
+#ifndef _VX_LENET_H
+#define _VX_LENET_H
+
+#include "tim/vx/operation.h"
+#include "tim/vx/tensor.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops.h"
+
+namespace acuitylite
+{
+
+class lenet
+{
+    public:
+        using input_0_type = uint8_t;
+        using output_0_type = uint16_t;
+        static std::vector<std::vector<uint32_t>> input_size_list;
+        static std::vector<uint32_t> input_bytes_list;
+        static std::vector<std::vector<uint32_t>> output_size_list;
+        static std::vector<std::shared_ptr<tim::vx::Tensor>> inputs_tensor;
+        static std::vector<std::shared_ptr<tim::vx::Tensor>> outputs_tensor;
+
+        static void construct_graph(std::shared_ptr<tim::vx::Graph> graph, const char *data_file_name);
+};
+
+}  // namespace acuitylite
+
+#endif
diff --git a/samples/multi_device/vx_mobilenet.cc b/samples/multi_device/vx_mobilenet.cc
new file mode 100644
index 0000000..fb55383
--- /dev/null
+++ b/samples/multi_device/vx_mobilenet.cc
@@ -0,0 +1,977 @@
+/****************************************************************************
+*   Generated by ACUITY 6.6.0
+*   Match timvx 1.1.30
+*
+*   Neural Network appliction network definition source file
+****************************************************************************/
+#include "vx_mobilenet.h"
+
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+namespace
+{
+
+char *get_const_data(const char *data_file_name)
+{
+    std::ifstream fin(data_file_name, std::ios::in | std::ios::binary);
+    if (fin)
+    {
+        fin.seekg(0, std::ios::end);
+        int size = fin.tellg();
+        fin.seekg(0, std::ios::beg);
+        char *buffer = new char [size];
+        std::cout<<"File "<<data_file_name <<" size:"<<size<<std::endl;
+        fin.read(buffer, size);
+        fin.close();
+        return buffer;
+    }
+    else
+    {
+        std::cout<<"Load file "<<data_file_name <<" failed"<<std::endl;
+        return NULL;
+    }
+}
+
+}  // namespace
+
+namespace acuitylite
+{
+
+std::vector<std::vector<uint32_t>> mobilenet::input_size_list = {{3 , 224 , 224 , 1}};
+std::vector<uint32_t> mobilenet::input_bytes_list = {3 * 224 * 224 * 1 * sizeof(input_0_type)};
+std::vector<std::vector<uint32_t>> mobilenet::output_size_list = {{1001 , 1}};
+std::vector<std::shared_ptr<tim::vx::Tensor>> mobilenet::inputs_tensor;
+std::vector<std::shared_ptr<tim::vx::Tensor>> mobilenet::outputs_tensor;
+
+void mobilenet::construct_graph
+    (
+    std::shared_ptr<tim::vx::Graph> graph,
+    const char *data_file_name
+    )
+{
+    char *coef_data_ptr = get_const_data(data_file_name);
+
+    tim::vx::Quantization permute_33_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0078125, 128);
+    tim::vx::TensorSpec permute_33_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, permute_33_out0_quant);
+    auto permute_33_out0 = graph->CreateTensor(permute_33_out0_spec);
+
+    tim::vx::Quantization convolution_1_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_1_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_1_out0_quant);
+    auto convolution_1_out0 = graph->CreateTensor(convolution_1_out0_spec);
+
+    tim::vx::ShapeType convolution_1_weight_shape({3,3,3,32});
+    tim::vx::Quantization convolution_1_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.02182667888700962, 151);
+    tim::vx::TensorSpec convolution_1_weight_spec(tim::vx::DataType::UINT8, convolution_1_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_1_weight_quant);
+    auto convolution_1_weight = graph->CreateTensor(convolution_1_weight_spec, coef_data_ptr + 1029156);
+
+    tim::vx::ShapeType convolution_1_bias_shape({32});
+    tim::vx::Quantization convolution_1_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00017052092880476266, 0);
+    tim::vx::TensorSpec convolution_1_bias_spec(tim::vx::DataType::INT32, convolution_1_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_1_bias_quant);
+    auto convolution_1_bias = graph->CreateTensor(convolution_1_bias_spec, coef_data_ptr + 1029028);
+
+    tim::vx::Quantization convolution_2_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_2_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_2_out0_quant);
+    auto convolution_2_out0 = graph->CreateTensor(convolution_2_out0_spec);
+
+    tim::vx::ShapeType convolution_2_weight_shape({3,3,32,1});
+    tim::vx::Quantization convolution_2_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.29219913482666016, 110);
+    tim::vx::TensorSpec convolution_2_weight_spec(tim::vx::DataType::UINT8, convolution_2_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_2_weight_quant);
+    auto convolution_2_weight = graph->CreateTensor(convolution_2_weight_spec, coef_data_ptr + 3172868);
+
+    tim::vx::ShapeType convolution_2_bias_shape({32});
+    tim::vx::Quantization convolution_2_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.006875000894069672, 0);
+    tim::vx::TensorSpec convolution_2_bias_spec(tim::vx::DataType::INT32, convolution_2_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_2_bias_quant);
+    auto convolution_2_bias = graph->CreateTensor(convolution_2_bias_spec, coef_data_ptr + 3172740);
+
+    tim::vx::Quantization convolution_3_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_3_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_3_out0_quant);
+    auto convolution_3_out0 = graph->CreateTensor(convolution_3_out0_spec);
+
+    tim::vx::ShapeType convolution_3_weight_shape({1,1,32,64});
+    tim::vx::Quantization convolution_3_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.030420949682593346, 121);
+    tim::vx::TensorSpec convolution_3_weight_spec(tim::vx::DataType::UINT8, convolution_3_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_3_weight_quant);
+    auto convolution_3_weight = graph->CreateTensor(convolution_3_weight_spec, coef_data_ptr + 3173412);
+
+    tim::vx::ShapeType convolution_3_bias_shape({64});
+    tim::vx::Quantization convolution_3_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0007157585932873189, 0);
+    tim::vx::TensorSpec convolution_3_bias_spec(tim::vx::DataType::INT32, convolution_3_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_3_bias_quant);
+    auto convolution_3_bias = graph->CreateTensor(convolution_3_bias_spec, coef_data_ptr + 3173156);
+
+    tim::vx::Quantization convolution_4_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_4_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_4_out0_quant);
+    auto convolution_4_out0 = graph->CreateTensor(convolution_4_out0_spec);
+
+    tim::vx::ShapeType convolution_4_weight_shape({3,3,64,1});
+    tim::vx::Quantization convolution_4_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.40277284383773804, 130);
+    tim::vx::TensorSpec convolution_4_weight_spec(tim::vx::DataType::UINT8, convolution_4_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_4_weight_quant);
+    auto convolution_4_weight = graph->CreateTensor(convolution_4_weight_spec, coef_data_ptr + 3175716);
+
+    tim::vx::ShapeType convolution_4_bias_shape({64});
+    tim::vx::Quantization convolution_4_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.009476631879806519, 0);
+    tim::vx::TensorSpec convolution_4_bias_spec(tim::vx::DataType::INT32, convolution_4_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_4_bias_quant);
+    auto convolution_4_bias = graph->CreateTensor(convolution_4_bias_spec, coef_data_ptr + 3175460);
+
+    tim::vx::Quantization convolution_5_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_5_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_5_out0_quant);
+    auto convolution_5_out0 = graph->CreateTensor(convolution_5_out0_spec);
+
+    tim::vx::ShapeType convolution_5_weight_shape({1,1,64,128});
+    tim::vx::Quantization convolution_5_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.015148180536925793, 104);
+    tim::vx::TensorSpec convolution_5_weight_spec(tim::vx::DataType::UINT8, convolution_5_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_5_weight_quant);
+    auto convolution_5_weight = graph->CreateTensor(convolution_5_weight_spec, coef_data_ptr + 3176804);
+
+    tim::vx::ShapeType convolution_5_bias_shape({128});
+    tim::vx::Quantization convolution_5_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00035641362774185836, 0);
+    tim::vx::TensorSpec convolution_5_bias_spec(tim::vx::DataType::INT32, convolution_5_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_5_bias_quant);
+    auto convolution_5_bias = graph->CreateTensor(convolution_5_bias_spec, coef_data_ptr + 3176292);
+
+    tim::vx::Quantization convolution_6_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_6_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_6_out0_quant);
+    auto convolution_6_out0 = graph->CreateTensor(convolution_6_out0_spec);
+
+    tim::vx::ShapeType convolution_6_weight_shape({3,3,128,1});
+    tim::vx::Quantization convolution_6_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.06053730100393295, 160);
+    tim::vx::TensorSpec convolution_6_weight_spec(tim::vx::DataType::UINT8, convolution_6_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_6_weight_quant);
+    auto convolution_6_weight = graph->CreateTensor(convolution_6_weight_spec, coef_data_ptr + 3185508);
+
+    tim::vx::ShapeType convolution_6_bias_shape({128});
+    tim::vx::Quantization convolution_6_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00142435054294765, 0);
+    tim::vx::TensorSpec convolution_6_bias_spec(tim::vx::DataType::INT32, convolution_6_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_6_bias_quant);
+    auto convolution_6_bias = graph->CreateTensor(convolution_6_bias_spec, coef_data_ptr + 3184996);
+
+    tim::vx::Quantization convolution_7_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_7_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_7_out0_quant);
+    auto convolution_7_out0 = graph->CreateTensor(convolution_7_out0_spec);
+
+    tim::vx::ShapeType convolution_7_weight_shape({1,1,128,128});
+    tim::vx::Quantization convolution_7_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.013755458407104015, 94);
+    tim::vx::TensorSpec convolution_7_weight_spec(tim::vx::DataType::UINT8, convolution_7_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_7_weight_quant);
+    auto convolution_7_weight = graph->CreateTensor(convolution_7_weight_spec, coef_data_ptr + 3187172);
+
+    tim::vx::ShapeType convolution_7_bias_shape({128});
+    tim::vx::Quantization convolution_7_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00032364498474635184, 0);
+    tim::vx::TensorSpec convolution_7_bias_spec(tim::vx::DataType::INT32, convolution_7_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_7_bias_quant);
+    auto convolution_7_bias = graph->CreateTensor(convolution_7_bias_spec, coef_data_ptr + 3186660);
+
+    tim::vx::Quantization convolution_8_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_8_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_8_out0_quant);
+    auto convolution_8_out0 = graph->CreateTensor(convolution_8_out0_spec);
+
+    tim::vx::ShapeType convolution_8_weight_shape({3,3,128,1});
+    tim::vx::Quantization convolution_8_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.01675807684659958, 123);
+    tim::vx::TensorSpec convolution_8_weight_spec(tim::vx::DataType::UINT8, convolution_8_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_8_weight_quant);
+    auto convolution_8_weight = graph->CreateTensor(convolution_8_weight_spec, coef_data_ptr + 3204068);
+
+    tim::vx::ShapeType convolution_8_bias_shape({128});
+    tim::vx::Quantization convolution_8_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0003942920302506536, 0);
+    tim::vx::TensorSpec convolution_8_bias_spec(tim::vx::DataType::INT32, convolution_8_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_8_bias_quant);
+    auto convolution_8_bias = graph->CreateTensor(convolution_8_bias_spec, coef_data_ptr + 3203556);
+
+    tim::vx::Quantization convolution_9_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_9_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_9_out0_quant);
+    auto convolution_9_out0 = graph->CreateTensor(convolution_9_out0_spec);
+
+    tim::vx::ShapeType convolution_9_weight_shape({1,1,128,256});
+    tim::vx::Quantization convolution_9_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.007601846940815449, 151);
+    tim::vx::TensorSpec convolution_9_weight_spec(tim::vx::DataType::UINT8, convolution_9_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_9_weight_quant);
+    auto convolution_9_weight = graph->CreateTensor(convolution_9_weight_spec, coef_data_ptr + 3206244);
+
+    tim::vx::ShapeType convolution_9_bias_shape({256});
+    tim::vx::Quantization convolution_9_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00017885988927446306, 0);
+    tim::vx::TensorSpec convolution_9_bias_spec(tim::vx::DataType::INT32, convolution_9_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_9_bias_quant);
+    auto convolution_9_bias = graph->CreateTensor(convolution_9_bias_spec, coef_data_ptr + 3205220);
+
+    tim::vx::Quantization convolution_10_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_10_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_10_out0_quant);
+    auto convolution_10_out0 = graph->CreateTensor(convolution_10_out0_spec);
+
+    tim::vx::ShapeType convolution_10_weight_shape({3,3,256,1});
+    tim::vx::Quantization convolution_10_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.04105526953935623, 129);
+    tim::vx::TensorSpec convolution_10_weight_spec(tim::vx::DataType::UINT8, convolution_10_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_10_weight_quant);
+    auto convolution_10_weight = graph->CreateTensor(convolution_10_weight_spec, coef_data_ptr + 3240036);
+
+    tim::vx::ShapeType convolution_10_bias_shape({256});
+    tim::vx::Quantization convolution_10_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0009659679490141571, 0);
+    tim::vx::TensorSpec convolution_10_bias_spec(tim::vx::DataType::INT32, convolution_10_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_10_bias_quant);
+    auto convolution_10_bias = graph->CreateTensor(convolution_10_bias_spec, coef_data_ptr + 3239012);
+
+    tim::vx::Quantization convolution_11_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_11_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_11_out0_quant);
+    auto convolution_11_out0 = graph->CreateTensor(convolution_11_out0_spec);
+
+    tim::vx::ShapeType convolution_11_weight_shape({1,1,256,256});
+    tim::vx::Quantization convolution_11_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.006431614048779011, 122);
+    tim::vx::TensorSpec convolution_11_weight_spec(tim::vx::DataType::UINT8, convolution_11_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_11_weight_quant);
+    auto convolution_11_weight = graph->CreateTensor(convolution_11_weight_spec, coef_data_ptr + 3243364);
+
+    tim::vx::ShapeType convolution_11_bias_shape({256});
+    tim::vx::Quantization convolution_11_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00015132607950363308, 0);
+    tim::vx::TensorSpec convolution_11_bias_spec(tim::vx::DataType::INT32, convolution_11_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_11_bias_quant);
+    auto convolution_11_bias = graph->CreateTensor(convolution_11_bias_spec, coef_data_ptr + 3242340);
+
+    tim::vx::Quantization convolution_12_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_12_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_12_out0_quant);
+    auto convolution_12_out0 = graph->CreateTensor(convolution_12_out0_spec);
+
+    tim::vx::ShapeType convolution_12_weight_shape({3,3,256,1});
+    tim::vx::Quantization convolution_12_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.013460792601108551, 122);
+    tim::vx::TensorSpec convolution_12_weight_spec(tim::vx::DataType::UINT8, convolution_12_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_12_weight_quant);
+    auto convolution_12_weight = graph->CreateTensor(convolution_12_weight_spec, coef_data_ptr + 3309924);
+
+    tim::vx::ShapeType convolution_12_bias_shape({256});
+    tim::vx::Quantization convolution_12_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0003167119575664401, 0);
+    tim::vx::TensorSpec convolution_12_bias_spec(tim::vx::DataType::INT32, convolution_12_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_12_bias_quant);
+    auto convolution_12_bias = graph->CreateTensor(convolution_12_bias_spec, coef_data_ptr + 3308900);
+
+    tim::vx::Quantization convolution_13_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_13_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_13_out0_quant);
+    auto convolution_13_out0 = graph->CreateTensor(convolution_13_out0_spec);
+
+    tim::vx::ShapeType convolution_13_weight_shape({1,1,256,512});
+    tim::vx::Quantization convolution_13_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.00917122047394514, 109);
+    tim::vx::TensorSpec convolution_13_weight_spec(tim::vx::DataType::UINT8, convolution_13_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_13_weight_quant);
+    auto convolution_13_weight = graph->CreateTensor(convolution_13_weight_spec, coef_data_ptr + 3314276);
+
+    tim::vx::ShapeType convolution_13_bias_shape({512});
+    tim::vx::Quantization convolution_13_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00021578485029749572, 0);
+    tim::vx::TensorSpec convolution_13_bias_spec(tim::vx::DataType::INT32, convolution_13_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_13_bias_quant);
+    auto convolution_13_bias = graph->CreateTensor(convolution_13_bias_spec, coef_data_ptr + 3312228);
+
+    tim::vx::Quantization convolution_14_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_14_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_14_out0_quant);
+    auto convolution_14_out0 = graph->CreateTensor(convolution_14_out0_spec);
+
+    tim::vx::ShapeType convolution_14_weight_shape({3,3,512,1});
+    tim::vx::Quantization convolution_14_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.036934755742549896, 132);
+    tim::vx::TensorSpec convolution_14_weight_spec(tim::vx::DataType::UINT8, convolution_14_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_14_weight_quant);
+    auto convolution_14_weight = graph->CreateTensor(convolution_14_weight_spec, coef_data_ptr + 3447396);
+
+    tim::vx::ShapeType convolution_14_bias_shape({512});
+    tim::vx::Quantization convolution_14_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0008690185495652258, 0);
+    tim::vx::TensorSpec convolution_14_bias_spec(tim::vx::DataType::INT32, convolution_14_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_14_bias_quant);
+    auto convolution_14_bias = graph->CreateTensor(convolution_14_bias_spec, coef_data_ptr + 3445348);
+
+    tim::vx::Quantization convolution_15_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_15_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_15_out0_quant);
+    auto convolution_15_out0 = graph->CreateTensor(convolution_15_out0_spec);
+
+    tim::vx::ShapeType convolution_15_weight_shape({1,1,512,512});
+    tim::vx::Quantization convolution_15_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.005300046876072884, 140);
+    tim::vx::TensorSpec convolution_15_weight_spec(tim::vx::DataType::UINT8, convolution_15_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_15_weight_quant);
+    auto convolution_15_weight = graph->CreateTensor(convolution_15_weight_spec, coef_data_ptr + 3454052);
+
+    tim::vx::ShapeType convolution_15_bias_shape({512});
+    tim::vx::Quantization convolution_15_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00012470202636905015, 0);
+    tim::vx::TensorSpec convolution_15_bias_spec(tim::vx::DataType::INT32, convolution_15_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_15_bias_quant);
+    auto convolution_15_bias = graph->CreateTensor(convolution_15_bias_spec, coef_data_ptr + 3452004);
+
+    tim::vx::Quantization convolution_16_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_16_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_16_out0_quant);
+    auto convolution_16_out0 = graph->CreateTensor(convolution_16_out0_spec);
+
+    tim::vx::ShapeType convolution_16_weight_shape({3,3,512,1});
+    tim::vx::Quantization convolution_16_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.042609862983226776, 94);
+    tim::vx::TensorSpec convolution_16_weight_spec(tim::vx::DataType::UINT8, convolution_16_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_16_weight_quant);
+    auto convolution_16_weight = graph->CreateTensor(convolution_16_weight_spec, coef_data_ptr + 3718244);
+
+    tim::vx::ShapeType convolution_16_bias_shape({512});
+    tim::vx::Quantization convolution_16_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0010025452356785536, 0);
+    tim::vx::TensorSpec convolution_16_bias_spec(tim::vx::DataType::INT32, convolution_16_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_16_bias_quant);
+    auto convolution_16_bias = graph->CreateTensor(convolution_16_bias_spec, coef_data_ptr + 3716196);
+
+    tim::vx::Quantization convolution_17_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_17_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_17_out0_quant);
+    auto convolution_17_out0 = graph->CreateTensor(convolution_17_out0_spec);
+
+    tim::vx::ShapeType convolution_17_weight_shape({1,1,512,512});
+    tim::vx::Quantization convolution_17_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0049632852897048, 127);
+    tim::vx::TensorSpec convolution_17_weight_spec(tim::vx::DataType::UINT8, convolution_17_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_17_weight_quant);
+    auto convolution_17_weight = graph->CreateTensor(convolution_17_weight_spec, coef_data_ptr + 3724900);
+
+    tim::vx::ShapeType convolution_17_bias_shape({512});
+    tim::vx::Quantization convolution_17_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011677854490699247, 0);
+    tim::vx::TensorSpec convolution_17_bias_spec(tim::vx::DataType::INT32, convolution_17_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_17_bias_quant);
+    auto convolution_17_bias = graph->CreateTensor(convolution_17_bias_spec, coef_data_ptr + 3722852);
+
+    tim::vx::Quantization convolution_18_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_18_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_18_out0_quant);
+    auto convolution_18_out0 = graph->CreateTensor(convolution_18_out0_spec);
+
+    tim::vx::ShapeType convolution_18_weight_shape({3,3,512,1});
+    tim::vx::Quantization convolution_18_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.028358859941363335, 127);
+    tim::vx::TensorSpec convolution_18_weight_spec(tim::vx::DataType::UINT8, convolution_18_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_18_weight_quant);
+    auto convolution_18_weight = graph->CreateTensor(convolution_18_weight_spec, coef_data_ptr + 3989092);
+
+    tim::vx::ShapeType convolution_18_bias_shape({512});
+    tim::vx::Quantization convolution_18_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0006672407616861165, 0);
+    tim::vx::TensorSpec convolution_18_bias_spec(tim::vx::DataType::INT32, convolution_18_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_18_bias_quant);
+    auto convolution_18_bias = graph->CreateTensor(convolution_18_bias_spec, coef_data_ptr + 3987044);
+
+    tim::vx::Quantization convolution_19_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_19_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_19_out0_quant);
+    auto convolution_19_out0 = graph->CreateTensor(convolution_19_out0_spec);
+
+    tim::vx::ShapeType convolution_19_weight_shape({1,1,512,512});
+    tim::vx::Quantization convolution_19_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.007770895957946777, 89);
+    tim::vx::TensorSpec convolution_19_weight_spec(tim::vx::DataType::UINT8, convolution_19_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_19_weight_quant);
+    auto convolution_19_weight = graph->CreateTensor(convolution_19_weight_spec, coef_data_ptr + 3995748);
+
+    tim::vx::ShapeType convolution_19_bias_shape({512});
+    tim::vx::Quantization convolution_19_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00018283734971191734, 0);
+    tim::vx::TensorSpec convolution_19_bias_spec(tim::vx::DataType::INT32, convolution_19_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_19_bias_quant);
+    auto convolution_19_bias = graph->CreateTensor(convolution_19_bias_spec, coef_data_ptr + 3993700);
+
+    tim::vx::Quantization convolution_20_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_20_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_20_out0_quant);
+    auto convolution_20_out0 = graph->CreateTensor(convolution_20_out0_spec);
+
+    tim::vx::ShapeType convolution_20_weight_shape({3,3,512,1});
+    tim::vx::Quantization convolution_20_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.024329448118805885, 134);
+    tim::vx::TensorSpec convolution_20_weight_spec(tim::vx::DataType::UINT8, convolution_20_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_20_weight_quant);
+    auto convolution_20_weight = graph->CreateTensor(convolution_20_weight_spec, coef_data_ptr + 1032068);
+
+    tim::vx::ShapeType convolution_20_bias_shape({512});
+    tim::vx::Quantization convolution_20_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0005724348593503237, 0);
+    tim::vx::TensorSpec convolution_20_bias_spec(tim::vx::DataType::INT32, convolution_20_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_20_bias_quant);
+    auto convolution_20_bias = graph->CreateTensor(convolution_20_bias_spec, coef_data_ptr + 1030020);
+
+    tim::vx::Quantization convolution_21_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_21_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_21_out0_quant);
+    auto convolution_21_out0 = graph->CreateTensor(convolution_21_out0_spec);
+
+    tim::vx::ShapeType convolution_21_weight_shape({1,1,512,512});
+    tim::vx::Quantization convolution_21_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.009658650495111942, 99);
+    tim::vx::TensorSpec convolution_21_weight_spec(tim::vx::DataType::UINT8, convolution_21_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_21_weight_quant);
+    auto convolution_21_weight = graph->CreateTensor(convolution_21_weight_spec, coef_data_ptr + 1038724);
+
+    tim::vx::ShapeType convolution_21_bias_shape({512});
+    tim::vx::Quantization convolution_21_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00022725333110429347, 0);
+    tim::vx::TensorSpec convolution_21_bias_spec(tim::vx::DataType::INT32, convolution_21_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_21_bias_quant);
+    auto convolution_21_bias = graph->CreateTensor(convolution_21_bias_spec, coef_data_ptr + 1036676);
+
+    tim::vx::Quantization convolution_22_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_22_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_22_out0_quant);
+    auto convolution_22_out0 = graph->CreateTensor(convolution_22_out0_spec);
+
+    tim::vx::ShapeType convolution_22_weight_shape({3,3,512,1});
+    tim::vx::Quantization convolution_22_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.019366811960935593, 106);
+    tim::vx::TensorSpec convolution_22_weight_spec(tim::vx::DataType::UINT8, convolution_22_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_22_weight_quant);
+    auto convolution_22_weight = graph->CreateTensor(convolution_22_weight_spec, coef_data_ptr + 1302916);
+
+    tim::vx::ShapeType convolution_22_bias_shape({512});
+    tim::vx::Quantization convolution_22_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0004556716012302786, 0);
+    tim::vx::TensorSpec convolution_22_bias_spec(tim::vx::DataType::INT32, convolution_22_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_22_bias_quant);
+    auto convolution_22_bias = graph->CreateTensor(convolution_22_bias_spec, coef_data_ptr + 1300868);
+
+    tim::vx::Quantization convolution_23_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_23_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_23_out0_quant);
+    auto convolution_23_out0 = graph->CreateTensor(convolution_23_out0_spec);
+
+    tim::vx::ShapeType convolution_23_weight_shape({1,1,512,512});
+    tim::vx::Quantization convolution_23_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.005446993745863438, 153);
+    tim::vx::TensorSpec convolution_23_weight_spec(tim::vx::DataType::UINT8, convolution_23_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_23_weight_quant);
+    auto convolution_23_weight = graph->CreateTensor(convolution_23_weight_spec, coef_data_ptr + 1309572);
+
+    tim::vx::ShapeType convolution_23_bias_shape({512});
+    tim::vx::Quantization convolution_23_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00012815947411581874, 0);
+    tim::vx::TensorSpec convolution_23_bias_spec(tim::vx::DataType::INT32, convolution_23_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_23_bias_quant);
+    auto convolution_23_bias = graph->CreateTensor(convolution_23_bias_spec, coef_data_ptr + 1307524);
+
+    tim::vx::Quantization convolution_24_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_24_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_24_out0_quant);
+    auto convolution_24_out0 = graph->CreateTensor(convolution_24_out0_spec);
+
+    tim::vx::ShapeType convolution_24_weight_shape({3,3,512,1});
+    tim::vx::Quantization convolution_24_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.007835594937205315, 126);
+    tim::vx::TensorSpec convolution_24_weight_spec(tim::vx::DataType::UINT8, convolution_24_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_24_weight_quant);
+    auto convolution_24_weight = graph->CreateTensor(convolution_24_weight_spec, coef_data_ptr + 1573764);
+
+    tim::vx::ShapeType convolution_24_bias_shape({512});
+    tim::vx::Quantization convolution_24_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00018435961101204157, 0);
+    tim::vx::TensorSpec convolution_24_bias_spec(tim::vx::DataType::INT32, convolution_24_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_24_bias_quant);
+    auto convolution_24_bias = graph->CreateTensor(convolution_24_bias_spec, coef_data_ptr + 1571716);
+
+    tim::vx::Quantization convolution_25_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_25_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_25_out0_quant);
+    auto convolution_25_out0 = graph->CreateTensor(convolution_25_out0_spec);
+
+    tim::vx::ShapeType convolution_25_weight_shape({1,1,512,1024});
+    tim::vx::Quantization convolution_25_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.00817922968417406, 130);
+    tim::vx::TensorSpec convolution_25_weight_spec(tim::vx::DataType::UINT8, convolution_25_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_25_weight_quant);
+    auto convolution_25_weight = graph->CreateTensor(convolution_25_weight_spec, coef_data_ptr + 1582468);
+
+    tim::vx::ShapeType convolution_25_bias_shape({1024});
+    tim::vx::Quantization convolution_25_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001924448151839897, 0);
+    tim::vx::TensorSpec convolution_25_bias_spec(tim::vx::DataType::INT32, convolution_25_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_25_bias_quant);
+    auto convolution_25_bias = graph->CreateTensor(convolution_25_bias_spec, coef_data_ptr + 1578372);
+
+    tim::vx::Quantization convolution_26_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_26_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_26_out0_quant);
+    auto convolution_26_out0 = graph->CreateTensor(convolution_26_out0_spec);
+
+    tim::vx::ShapeType convolution_26_weight_shape({3,3,1024,1});
+    tim::vx::Quantization convolution_26_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.12616927921772003, 211);
+    tim::vx::TensorSpec convolution_26_weight_spec(tim::vx::DataType::UINT8, convolution_26_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_26_weight_quant);
+    auto convolution_26_weight = graph->CreateTensor(convolution_26_weight_spec, coef_data_ptr + 2110852);
+
+    tim::vx::ShapeType convolution_26_bias_shape({1024});
+    tim::vx::Quantization convolution_26_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.002968570915982127, 0);
+    tim::vx::TensorSpec convolution_26_bias_spec(tim::vx::DataType::INT32, convolution_26_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_26_bias_quant);
+    auto convolution_26_bias = graph->CreateTensor(convolution_26_bias_spec, coef_data_ptr + 2106756);
+
+    tim::vx::Quantization convolution_27_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec convolution_27_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_27_out0_quant);
+    auto convolution_27_out0 = graph->CreateTensor(convolution_27_out0_spec);
+
+    tim::vx::ShapeType convolution_27_weight_shape({1,1,1024,1024});
+    tim::vx::Quantization convolution_27_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.018048152327537537, 95);
+    tim::vx::TensorSpec convolution_27_weight_spec(tim::vx::DataType::UINT8, convolution_27_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_27_weight_quant);
+    auto convolution_27_weight = graph->CreateTensor(convolution_27_weight_spec, coef_data_ptr + 2124164);
+
+    tim::vx::ShapeType convolution_27_bias_shape({1024});
+    tim::vx::Quantization convolution_27_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.000424645550083369, 0);
+    tim::vx::TensorSpec convolution_27_bias_spec(tim::vx::DataType::INT32, convolution_27_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_27_bias_quant);
+    auto convolution_27_bias = graph->CreateTensor(convolution_27_bias_spec, coef_data_ptr + 2120068);
+
+    tim::vx::Quantization pooling_28_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023528477177023888, 0);
+    tim::vx::TensorSpec pooling_28_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, pooling_28_out0_quant);
+    auto pooling_28_out0 = graph->CreateTensor(pooling_28_out0_spec);
+
+    tim::vx::Quantization convolution_29_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16609922051429749, 66);
+    tim::vx::TensorSpec convolution_29_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_29_out0_quant);
+    auto convolution_29_out0 = graph->CreateTensor(convolution_29_out0_spec);
+
+    tim::vx::ShapeType convolution_29_weight_shape({1,1,1024,1001});
+    tim::vx::Quantization convolution_29_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.004986600950360298, 74);
+    tim::vx::TensorSpec convolution_29_weight_spec(tim::vx::DataType::UINT8, convolution_29_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_29_weight_quant);
+    auto convolution_29_weight = graph->CreateTensor(convolution_29_weight_spec, coef_data_ptr + 4004);
+
+    tim::vx::ShapeType convolution_29_bias_shape({1001});
+    tim::vx::Quantization convolution_29_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011732713028322905, 0);
+    tim::vx::TensorSpec convolution_29_bias_spec(tim::vx::DataType::INT32, convolution_29_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_29_bias_quant);
+    auto convolution_29_bias = graph->CreateTensor(convolution_29_bias_spec, coef_data_ptr + 0);
+
+    tim::vx::Quantization permute_34_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16609922051429749, 66);
+    tim::vx::TensorSpec permute_34_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, permute_34_out0_quant);
+    auto permute_34_out0 = graph->CreateTensor(permute_34_out0_spec);
+
+    tim::vx::Quantization reshape_30_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16609922051429749, 66);
+    tim::vx::TensorSpec reshape_30_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, reshape_30_out0_quant);
+    auto reshape_30_out0 = graph->CreateTensor(reshape_30_out0_spec);
+
+    tim::vx::ShapeType input_0_shape({3,224,224,1});
+    tim::vx::Quantization input_0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0078125, 128);
+    tim::vx::TensorSpec input_0_spec(tim::vx::DataType::UINT8, input_0_shape,
+    tim::vx::TensorAttribute::INPUT, input_0_quant);
+    auto input_0 = graph->CreateTensor(input_0_spec);
+
+    tim::vx::ShapeType output_32_shape({1001,1});
+    // tim::vx::Quantization output_32_quant(tim::vx::QuantType::ASYMMETRIC, 0.00390625, 0);
+    // tim::vx::TensorSpec output_32_spec(tim::vx::DataType::UINT8, output_32_shape,
+    // tim::vx::TensorAttribute::OUTPUT, output_32_quant);
+    tim::vx::TensorSpec output_32_spec(tim::vx::DataType::FLOAT32, output_32_shape,
+    tim::vx::TensorAttribute::OUTPUT);
+    auto output_32 = graph->CreateTensor(output_32_spec);
+
+    mobilenet::inputs_tensor.push_back(input_0);
+
+    mobilenet::outputs_tensor.push_back(output_32);
+
+    auto permute_33 = graph->CreateOperation <tim::vx::ops::Transpose>(
+        std::vector<uint32_t>({1,2,0,3}));    // perm
+
+    auto convolution_1 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        32,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,1,0,1}),    // pad
+        0);    // multiplier
+
+    auto convolution_2 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        32,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_3 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_4 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,1,0,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_5 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_6 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_7 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_8 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,1,0,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_9 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_10 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_11 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_12 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,1,0,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_13 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_14 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_15 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_16 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_17 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_18 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_19 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_20 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_21 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_22 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_23 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_24 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,1,0,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_25 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_26 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        1.0);    // multiplier
+
+    auto convolution_27 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto pooling_28 = graph->CreateOperation <tim::vx::ops::Pool2d>(
+        tim::vx::PoolType::AVG,    // type
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        std::array<uint32_t, 2>({7,7}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        tim::vx::RoundType::FLOOR);    // round_type
+
+    auto convolution_29 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1001,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto permute_34 = graph->CreateOperation <tim::vx::ops::Transpose>(
+        std::vector<uint32_t>({2,0,1,3}));    // perm
+
+    auto reshape_30 = graph->CreateOperation <tim::vx::ops::Reshape>(
+        std::vector<uint32_t>({1001,1}));    // size
+
+    auto softmax_31 = graph->CreateOperation <tim::vx::ops::Softmax>(
+        1.0,    // beta
+        0);    // axis
+
+    (*permute_33)
+        .BindInputs({input_0})
+        .BindOutputs({permute_33_out0});
+
+    (*convolution_1)
+        .BindInputs({permute_33_out0, convolution_1_weight, convolution_1_bias})
+        .BindOutputs({convolution_1_out0});
+
+    (*convolution_2)
+        .BindInputs({convolution_1_out0, convolution_2_weight, convolution_2_bias})
+        .BindOutputs({convolution_2_out0});
+
+    (*convolution_3)
+        .BindInputs({convolution_2_out0, convolution_3_weight, convolution_3_bias})
+        .BindOutputs({convolution_3_out0});
+
+    (*convolution_4)
+        .BindInputs({convolution_3_out0, convolution_4_weight, convolution_4_bias})
+        .BindOutputs({convolution_4_out0});
+
+    (*convolution_5)
+        .BindInputs({convolution_4_out0, convolution_5_weight, convolution_5_bias})
+        .BindOutputs({convolution_5_out0});
+
+    (*convolution_6)
+        .BindInputs({convolution_5_out0, convolution_6_weight, convolution_6_bias})
+        .BindOutputs({convolution_6_out0});
+
+    (*convolution_7)
+        .BindInputs({convolution_6_out0, convolution_7_weight, convolution_7_bias})
+        .BindOutputs({convolution_7_out0});
+
+    (*convolution_8)
+        .BindInputs({convolution_7_out0, convolution_8_weight, convolution_8_bias})
+        .BindOutputs({convolution_8_out0});
+
+    (*convolution_9)
+        .BindInputs({convolution_8_out0, convolution_9_weight, convolution_9_bias})
+        .BindOutputs({convolution_9_out0});
+
+    (*convolution_10)
+        .BindInputs({convolution_9_out0, convolution_10_weight, convolution_10_bias})
+        .BindOutputs({convolution_10_out0});
+
+    (*convolution_11)
+        .BindInputs({convolution_10_out0, convolution_11_weight, convolution_11_bias})
+        .BindOutputs({convolution_11_out0});
+
+    (*convolution_12)
+        .BindInputs({convolution_11_out0, convolution_12_weight, convolution_12_bias})
+        .BindOutputs({convolution_12_out0});
+
+    (*convolution_13)
+        .BindInputs({convolution_12_out0, convolution_13_weight, convolution_13_bias})
+        .BindOutputs({convolution_13_out0});
+
+    (*convolution_14)
+        .BindInputs({convolution_13_out0, convolution_14_weight, convolution_14_bias})
+        .BindOutputs({convolution_14_out0});
+
+    (*convolution_15)
+        .BindInputs({convolution_14_out0, convolution_15_weight, convolution_15_bias})
+        .BindOutputs({convolution_15_out0});
+
+    (*convolution_16)
+        .BindInputs({convolution_15_out0, convolution_16_weight, convolution_16_bias})
+        .BindOutputs({convolution_16_out0});
+
+    (*convolution_17)
+        .BindInputs({convolution_16_out0, convolution_17_weight, convolution_17_bias})
+        .BindOutputs({convolution_17_out0});
+
+    (*convolution_18)
+        .BindInputs({convolution_17_out0, convolution_18_weight, convolution_18_bias})
+        .BindOutputs({convolution_18_out0});
+
+    (*convolution_19)
+        .BindInputs({convolution_18_out0, convolution_19_weight, convolution_19_bias})
+        .BindOutputs({convolution_19_out0});
+
+    (*convolution_20)
+        .BindInputs({convolution_19_out0, convolution_20_weight, convolution_20_bias})
+        .BindOutputs({convolution_20_out0});
+
+    (*convolution_21)
+        .BindInputs({convolution_20_out0, convolution_21_weight, convolution_21_bias})
+        .BindOutputs({convolution_21_out0});
+
+    (*convolution_22)
+        .BindInputs({convolution_21_out0, convolution_22_weight, convolution_22_bias})
+        .BindOutputs({convolution_22_out0});
+
+    (*convolution_23)
+        .BindInputs({convolution_22_out0, convolution_23_weight, convolution_23_bias})
+        .BindOutputs({convolution_23_out0});
+
+    (*convolution_24)
+        .BindInputs({convolution_23_out0, convolution_24_weight, convolution_24_bias})
+        .BindOutputs({convolution_24_out0});
+
+    (*convolution_25)
+        .BindInputs({convolution_24_out0, convolution_25_weight, convolution_25_bias})
+        .BindOutputs({convolution_25_out0});
+
+    (*convolution_26)
+        .BindInputs({convolution_25_out0, convolution_26_weight, convolution_26_bias})
+        .BindOutputs({convolution_26_out0});
+
+    (*convolution_27)
+        .BindInputs({convolution_26_out0, convolution_27_weight, convolution_27_bias})
+        .BindOutputs({convolution_27_out0});
+
+    (*pooling_28)
+        .BindInputs({convolution_27_out0})
+        .BindOutputs({pooling_28_out0});
+
+    (*convolution_29)
+        .BindInputs({pooling_28_out0, convolution_29_weight, convolution_29_bias})
+        .BindOutputs({convolution_29_out0});
+
+    (*permute_34)
+        .BindInputs({convolution_29_out0})
+        .BindOutputs({permute_34_out0});
+
+    (*reshape_30)
+        .BindInputs({permute_34_out0})
+        .BindOutputs({reshape_30_out0});
+
+    (*softmax_31)
+        .BindInputs({reshape_30_out0})
+        .BindOutputs({output_32});
+
+    free(coef_data_ptr);
+}
+
+}  // namespace acuitylite
diff --git a/samples/multi_device/vx_mobilenet.h b/samples/multi_device/vx_mobilenet.h
new file mode 100644
index 0000000..1f81509
--- /dev/null
+++ b/samples/multi_device/vx_mobilenet.h
@@ -0,0 +1,34 @@
+/****************************************************************************
+*   Generated by ACUITY 6.6.0
+*   Match timvx 1.1.30
+*
+*   Neural Network appliction network definition header file
+****************************************************************************/
+#ifndef _VX_MOBILENET_H
+#define _VX_MOBILENET_H
+
+#include "tim/vx/operation.h"
+#include "tim/vx/tensor.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops.h"
+
+namespace acuitylite
+{
+
+class mobilenet
+{
+    public:
+        using input_0_type = uint8_t;
+        using output_0_type = uint8_t;
+        static std::vector<std::vector<uint32_t>> input_size_list;
+        static std::vector<uint32_t> input_bytes_list;
+        static std::vector<std::vector<uint32_t>> output_size_list;
+        static std::vector<std::shared_ptr<tim::vx::Tensor>> inputs_tensor;
+        static std::vector<std::shared_ptr<tim::vx::Tensor>> outputs_tensor;
+
+        static void construct_graph(std::shared_ptr<tim::vx::Graph> graph, const char *data_file_name);
+};
+
+}  // namespace acuitylite
+
+#endif
diff --git a/samples/multi_device/vx_resnet50.cc b/samples/multi_device/vx_resnet50.cc
new file mode 100644
index 0000000..7011e3e
--- /dev/null
+++ b/samples/multi_device/vx_resnet50.cc
@@ -0,0 +1,2416 @@
+/****************************************************************************
+*   Generated by ACUITY 6.6.0
+*   Match timvx 1.1.30
+*
+*   Neural Network appliction network definition source file
+****************************************************************************/
+#include "vx_resnet50.h"
+
+#include <iostream>
+#include <fstream>
+#include <vector>
+
+namespace
+{
+
+char *get_const_data(const char *data_file_name)
+{
+    std::ifstream fin(data_file_name, std::ios::in | std::ios::binary);
+    if (fin)
+    {
+        fin.seekg(0, std::ios::end);
+        int size = fin.tellg();
+        fin.seekg(0, std::ios::beg);
+        char *buffer = new char [size];
+        std::cout<<"File "<<data_file_name <<" size:"<<size<<std::endl;
+        fin.read(buffer, size);
+        fin.close();
+        return buffer;
+    }
+    else
+    {
+        std::cout<<"Load file "<<data_file_name <<" failed"<<std::endl;
+        return NULL;
+    }
+}
+
+}  // namespace
+
+namespace acuitylite
+{
+
+std::vector<std::vector<uint32_t>> resnet50::input_size_list = {{224 , 224 , 3 , 1}};
+std::vector<uint32_t> resnet50::input_bytes_list = {224 * 224 * 3 * 1 * sizeof(input_0_type)};
+std::vector<std::vector<uint32_t>> resnet50::output_size_list = {{1000 , 1}};
+std::vector<std::shared_ptr<tim::vx::Tensor>> resnet50::inputs_tensor;
+std::vector<std::shared_ptr<tim::vx::Tensor>> resnet50::outputs_tensor;
+
+void resnet50::construct_graph
+    (
+    std::shared_ptr<tim::vx::Graph> graph,
+    const char *data_file_name
+    )
+{
+    char *coef_data_ptr = get_const_data(data_file_name);
+
+    tim::vx::Quantization convolution_1_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06513651460409164, 0);
+    tim::vx::TensorSpec convolution_1_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_1_out0_quant);
+    auto convolution_1_out0 = graph->CreateTensor(convolution_1_out0_spec);
+
+    tim::vx::ShapeType convolution_1_weight_shape({7,7,3,64});
+    tim::vx::Quantization convolution_1_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001737833663355559, 133);
+    tim::vx::TensorSpec convolution_1_weight_spec(tim::vx::DataType::UINT8, convolution_1_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_1_weight_quant);
+    auto convolution_1_weight = graph->CreateTensor(convolution_1_weight_spec, coef_data_ptr + 256);
+
+    tim::vx::ShapeType convolution_1_bias_shape({64});
+    tim::vx::Quantization convolution_1_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001737833663355559, 0);
+    tim::vx::TensorSpec convolution_1_bias_spec(tim::vx::DataType::INT32, convolution_1_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_1_bias_quant);
+    auto convolution_1_bias = graph->CreateTensor(convolution_1_bias_spec, coef_data_ptr + 0);
+
+    tim::vx::Quantization relu_4_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06513651460409164, 0);
+    tim::vx::TensorSpec relu_4_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_4_out0_quant);
+    auto relu_4_out0 = graph->CreateTensor(relu_4_out0_spec);
+
+    tim::vx::Quantization pooling_5_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06513651460409164, 0);
+    tim::vx::TensorSpec pooling_5_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, pooling_5_out0_quant);
+    auto pooling_5_out0 = graph->CreateTensor(pooling_5_out0_spec);
+
+    tim::vx::Quantization convolution_9_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.036857619881629944, 0);
+    tim::vx::TensorSpec convolution_9_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_9_out0_quant);
+    auto convolution_9_out0 = graph->CreateTensor(convolution_9_out0_spec);
+
+    tim::vx::ShapeType convolution_9_weight_shape({1,1,64,64});
+    tim::vx::Quantization convolution_9_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.004803352523595095, 175);
+    tim::vx::TensorSpec convolution_9_weight_spec(tim::vx::DataType::UINT8, convolution_9_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_9_weight_quant);
+    auto convolution_9_weight = graph->CreateTensor(convolution_9_weight_spec, coef_data_ptr + 2095712);
+
+    tim::vx::ShapeType convolution_9_bias_shape({64});
+    tim::vx::Quantization convolution_9_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00031287362799048424, 0);
+    tim::vx::TensorSpec convolution_9_bias_spec(tim::vx::DataType::INT32, convolution_9_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_9_bias_quant);
+    auto convolution_9_bias = graph->CreateTensor(convolution_9_bias_spec, coef_data_ptr + 2095456);
+
+    tim::vx::Quantization relu_12_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.036857619881629944, 0);
+    tim::vx::TensorSpec relu_12_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_12_out0_quant);
+    auto relu_12_out0 = graph->CreateTensor(relu_12_out0_spec);
+
+    tim::vx::Quantization convolution_13_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06513651460409164, 0);
+    tim::vx::TensorSpec convolution_13_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_13_out0_quant);
+    auto convolution_13_out0 = graph->CreateTensor(convolution_13_out0_spec);
+
+    tim::vx::ShapeType convolution_13_weight_shape({3,3,64,64});
+    tim::vx::Quantization convolution_13_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0022254411596804857, 118);
+    tim::vx::TensorSpec convolution_13_weight_spec(tim::vx::DataType::UINT8, convolution_13_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_13_weight_quant);
+    auto convolution_13_weight = graph->CreateTensor(convolution_13_weight_spec, coef_data_ptr + 2100064);
+
+    tim::vx::ShapeType convolution_13_bias_shape({64});
+    tim::vx::Quantization convolution_13_bias_quant(tim::vx::QuantType::ASYMMETRIC, 8.202446770155802e-05, 0);
+    tim::vx::TensorSpec convolution_13_bias_spec(tim::vx::DataType::INT32, convolution_13_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_13_bias_quant);
+    auto convolution_13_bias = graph->CreateTensor(convolution_13_bias_spec, coef_data_ptr + 2099808);
+
+    tim::vx::Quantization relu_16_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06513651460409164, 0);
+    tim::vx::TensorSpec relu_16_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_16_out0_quant);
+    auto relu_16_out0 = graph->CreateTensor(relu_16_out0_spec);
+
+    tim::vx::Quantization concat_231_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06513651460409164, 0);
+    tim::vx::TensorSpec concat_231_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, concat_231_out0_quant);
+    auto concat_231_out0 = graph->CreateTensor(concat_231_out0_spec);
+
+    tim::vx::Quantization convolution_232_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05724356323480606, 0);
+    tim::vx::TensorSpec convolution_232_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_232_out0_quant);
+    auto convolution_232_out0 = graph->CreateTensor(convolution_232_out0_spec);
+
+    tim::vx::ShapeType convolution_232_weight_shape({1,1,128,256});
+    tim::vx::Quantization convolution_232_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.013699613511562347, 128);
+    tim::vx::TensorSpec convolution_232_weight_spec(tim::vx::DataType::UINT8, convolution_232_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_232_weight_quant);
+    auto convolution_232_weight = graph->CreateTensor(convolution_232_weight_spec, coef_data_ptr + 2062688);
+
+    tim::vx::ShapeType convolution_232_bias_shape({256});
+    tim::vx::Quantization convolution_232_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0008923450950533152, 0);
+    tim::vx::TensorSpec convolution_232_bias_spec(tim::vx::DataType::INT32, convolution_232_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_232_bias_quant);
+    auto convolution_232_bias = graph->CreateTensor(convolution_232_bias_spec, coef_data_ptr + 2061664);
+
+    tim::vx::Quantization relu_21_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05724356323480606, 0);
+    tim::vx::TensorSpec relu_21_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_21_out0_quant);
+    auto relu_21_out0 = graph->CreateTensor(relu_21_out0_spec);
+
+    tim::vx::Quantization convolution_22_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.041045863181352615, 0);
+    tim::vx::TensorSpec convolution_22_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_22_out0_quant);
+    auto convolution_22_out0 = graph->CreateTensor(convolution_22_out0_spec);
+
+    tim::vx::ShapeType convolution_22_weight_shape({1,1,256,64});
+    tim::vx::Quantization convolution_22_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0044895135797560215, 147);
+    tim::vx::TensorSpec convolution_22_weight_spec(tim::vx::DataType::UINT8, convolution_22_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_22_weight_quant);
+    auto convolution_22_weight = graph->CreateTensor(convolution_22_weight_spec, coef_data_ptr + 2137184);
+
+    tim::vx::ShapeType convolution_22_bias_shape({64});
+    tim::vx::Quantization convolution_22_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0002569957578089088, 0);
+    tim::vx::TensorSpec convolution_22_bias_spec(tim::vx::DataType::INT32, convolution_22_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_22_bias_quant);
+    auto convolution_22_bias = graph->CreateTensor(convolution_22_bias_spec, coef_data_ptr + 2136928);
+
+    tim::vx::Quantization relu_25_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.041045863181352615, 0);
+    tim::vx::TensorSpec relu_25_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_25_out0_quant);
+    auto relu_25_out0 = graph->CreateTensor(relu_25_out0_spec);
+
+    tim::vx::Quantization convolution_26_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.033543918281793594, 0);
+    tim::vx::TensorSpec convolution_26_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_26_out0_quant);
+    auto convolution_26_out0 = graph->CreateTensor(convolution_26_out0_spec);
+
+    tim::vx::ShapeType convolution_26_weight_shape({3,3,64,64});
+    tim::vx::Quantization convolution_26_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0041098142974078655, 108);
+    tim::vx::TensorSpec convolution_26_weight_spec(tim::vx::DataType::UINT8, convolution_26_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_26_weight_quant);
+    auto convolution_26_weight = graph->CreateTensor(convolution_26_weight_spec, coef_data_ptr + 2153824);
+
+    tim::vx::ShapeType convolution_26_bias_shape({64});
+    tim::vx::Quantization convolution_26_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00016869087994564325, 0);
+    tim::vx::TensorSpec convolution_26_bias_spec(tim::vx::DataType::INT32, convolution_26_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_26_bias_quant);
+    auto convolution_26_bias = graph->CreateTensor(convolution_26_bias_spec, coef_data_ptr + 2153568);
+
+    tim::vx::Quantization relu_29_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.033543918281793594, 0);
+    tim::vx::TensorSpec relu_29_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_29_out0_quant);
+    auto relu_29_out0 = graph->CreateTensor(relu_29_out0_spec);
+
+    tim::vx::Quantization convolution_30_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.09178134053945541, 133);
+    tim::vx::TensorSpec convolution_30_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_30_out0_quant);
+    auto convolution_30_out0 = graph->CreateTensor(convolution_30_out0_spec);
+
+    tim::vx::ShapeType convolution_30_weight_shape({1,1,64,256});
+    tim::vx::Quantization convolution_30_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.01000303216278553, 111);
+    tim::vx::TensorSpec convolution_30_weight_spec(tim::vx::DataType::UINT8, convolution_30_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_30_weight_quant);
+    auto convolution_30_weight = graph->CreateTensor(convolution_30_weight_spec, coef_data_ptr + 2191712);
+
+    tim::vx::ShapeType convolution_30_bias_shape({256});
+    tim::vx::Quantization convolution_30_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00033554088440723717, 0);
+    tim::vx::TensorSpec convolution_30_bias_spec(tim::vx::DataType::INT32, convolution_30_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_30_bias_quant);
+    auto convolution_30_bias = graph->CreateTensor(convolution_30_bias_spec, coef_data_ptr + 2190688);
+
+    tim::vx::Quantization add_33_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05750435218214989, 0);
+    tim::vx::TensorSpec add_33_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_33_out0_quant);
+    auto add_33_out0 = graph->CreateTensor(add_33_out0_spec);
+
+    tim::vx::Quantization relu_34_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05750435218214989, 0);
+    tim::vx::TensorSpec relu_34_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_34_out0_quant);
+    auto relu_34_out0 = graph->CreateTensor(relu_34_out0_spec);
+
+    tim::vx::Quantization convolution_35_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03251691907644272, 0);
+    tim::vx::TensorSpec convolution_35_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_35_out0_quant);
+    auto convolution_35_out0 = graph->CreateTensor(convolution_35_out0_spec);
+
+    tim::vx::ShapeType convolution_35_weight_shape({1,1,256,64});
+    tim::vx::Quantization convolution_35_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0024991377722471952, 114);
+    tim::vx::TensorSpec convolution_35_weight_spec(tim::vx::DataType::UINT8, convolution_35_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_35_weight_quant);
+    auto convolution_35_weight = graph->CreateTensor(convolution_35_weight_spec, coef_data_ptr + 2208352);
+
+    tim::vx::ShapeType convolution_35_bias_shape({64});
+    tim::vx::Quantization convolution_35_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00014371129509527236, 0);
+    tim::vx::TensorSpec convolution_35_bias_spec(tim::vx::DataType::INT32, convolution_35_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_35_bias_quant);
+    auto convolution_35_bias = graph->CreateTensor(convolution_35_bias_spec, coef_data_ptr + 2208096);
+
+    tim::vx::Quantization relu_38_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03251691907644272, 0);
+    tim::vx::TensorSpec relu_38_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_38_out0_quant);
+    auto relu_38_out0 = graph->CreateTensor(relu_38_out0_spec);
+
+    tim::vx::Quantization convolution_39_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03924860432744026, 0);
+    tim::vx::TensorSpec convolution_39_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_39_out0_quant);
+    auto convolution_39_out0 = graph->CreateTensor(convolution_39_out0_spec);
+
+    tim::vx::ShapeType convolution_39_weight_shape({3,3,64,64});
+    tim::vx::Quantization convolution_39_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0032176407985389233, 128);
+    tim::vx::TensorSpec convolution_39_weight_spec(tim::vx::DataType::UINT8, convolution_39_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_39_weight_quant);
+    auto convolution_39_weight = graph->CreateTensor(convolution_39_weight_spec, coef_data_ptr + 2224992);
+
+    tim::vx::ShapeType convolution_39_bias_shape({64});
+    tim::vx::Quantization convolution_39_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00010462776845088229, 0);
+    tim::vx::TensorSpec convolution_39_bias_spec(tim::vx::DataType::INT32, convolution_39_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_39_bias_quant);
+    auto convolution_39_bias = graph->CreateTensor(convolution_39_bias_spec, coef_data_ptr + 2224736);
+
+    tim::vx::Quantization relu_42_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03924860432744026, 0);
+    tim::vx::TensorSpec relu_42_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_42_out0_quant);
+    auto relu_42_out0 = graph->CreateTensor(relu_42_out0_spec);
+
+    tim::vx::Quantization convolution_43_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.08585022389888763, 141);
+    tim::vx::TensorSpec convolution_43_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_43_out0_quant);
+    auto convolution_43_out0 = graph->CreateTensor(convolution_43_out0_spec);
+
+    tim::vx::ShapeType convolution_43_weight_shape({1,1,64,256});
+    tim::vx::Quantization convolution_43_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.008329907432198524, 108);
+    tim::vx::TensorSpec convolution_43_weight_spec(tim::vx::DataType::UINT8, convolution_43_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_43_weight_quant);
+    auto convolution_43_weight = graph->CreateTensor(convolution_43_weight_spec, coef_data_ptr + 2262880);
+
+    tim::vx::ShapeType convolution_43_bias_shape({256});
+    tim::vx::Quantization convolution_43_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.000326937239151448, 0);
+    tim::vx::TensorSpec convolution_43_bias_spec(tim::vx::DataType::INT32, convolution_43_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_43_bias_quant);
+    auto convolution_43_bias = graph->CreateTensor(convolution_43_bias_spec, coef_data_ptr + 2261856);
+
+    tim::vx::Quantization add_46_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05717748776078224, 0);
+    tim::vx::TensorSpec add_46_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_46_out0_quant);
+    auto add_46_out0 = graph->CreateTensor(add_46_out0_spec);
+
+    tim::vx::Quantization relu_47_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05717748776078224, 0);
+    tim::vx::TensorSpec relu_47_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_47_out0_quant);
+    auto relu_47_out0 = graph->CreateTensor(relu_47_out0_spec);
+
+    tim::vx::Quantization convolution_48_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.10674899071455002, 117);
+    tim::vx::TensorSpec convolution_48_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_48_out0_quant);
+    auto convolution_48_out0 = graph->CreateTensor(convolution_48_out0_spec);
+
+    tim::vx::ShapeType convolution_48_weight_shape({1,1,256,512});
+    tim::vx::Quantization convolution_48_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.006406530737876892, 147);
+    tim::vx::TensorSpec convolution_48_weight_spec(tim::vx::DataType::UINT8, convolution_48_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_48_weight_quant);
+    auto convolution_48_weight = graph->CreateTensor(convolution_48_weight_spec, coef_data_ptr + 2281312);
+
+    tim::vx::ShapeType convolution_48_bias_shape({512});
+    tim::vx::Quantization convolution_48_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0003663093375507742, 0);
+    tim::vx::TensorSpec convolution_48_bias_spec(tim::vx::DataType::INT32, convolution_48_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_48_bias_quant);
+    auto convolution_48_bias = graph->CreateTensor(convolution_48_bias_spec, coef_data_ptr + 2279264);
+
+    tim::vx::Quantization convolution_51_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.025104688480496407, 0);
+    tim::vx::TensorSpec convolution_51_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_51_out0_quant);
+    auto convolution_51_out0 = graph->CreateTensor(convolution_51_out0_spec);
+
+    tim::vx::ShapeType convolution_51_weight_shape({1,1,256,128});
+    tim::vx::Quantization convolution_51_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0030040210112929344, 152);
+    tim::vx::TensorSpec convolution_51_weight_spec(tim::vx::DataType::UINT8, convolution_51_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_51_weight_quant);
+    auto convolution_51_weight = graph->CreateTensor(convolution_51_weight_spec, coef_data_ptr + 2412896);
+
+    tim::vx::ShapeType convolution_51_bias_shape({128});
+    tim::vx::Quantization convolution_51_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001717623817967251, 0);
+    tim::vx::TensorSpec convolution_51_bias_spec(tim::vx::DataType::INT32, convolution_51_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_51_bias_quant);
+    auto convolution_51_bias = graph->CreateTensor(convolution_51_bias_spec, coef_data_ptr + 2412384);
+
+    tim::vx::Quantization relu_54_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.025104688480496407, 0);
+    tim::vx::TensorSpec relu_54_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_54_out0_quant);
+    auto relu_54_out0 = graph->CreateTensor(relu_54_out0_spec);
+
+    tim::vx::Quantization convolution_55_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.024383682757616043, 0);
+    tim::vx::TensorSpec convolution_55_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_55_out0_quant);
+    auto convolution_55_out0 = graph->CreateTensor(convolution_55_out0_spec);
+
+    tim::vx::ShapeType convolution_55_weight_shape({3,3,128,128});
+    tim::vx::Quantization convolution_55_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.003081039758399129, 129);
+    tim::vx::TensorSpec convolution_55_weight_spec(tim::vx::DataType::UINT8, convolution_55_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_55_weight_quant);
+    auto convolution_55_weight = graph->CreateTensor(convolution_55_weight_spec, coef_data_ptr + 2446176);
+
+    tim::vx::ShapeType convolution_55_bias_shape({128});
+    tim::vx::Quantization convolution_55_bias_quant(tim::vx::QuantType::ASYMMETRIC, 7.734854443697259e-05, 0);
+    tim::vx::TensorSpec convolution_55_bias_spec(tim::vx::DataType::INT32, convolution_55_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_55_bias_quant);
+    auto convolution_55_bias = graph->CreateTensor(convolution_55_bias_spec, coef_data_ptr + 2445664);
+
+    tim::vx::Quantization relu_58_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.024383682757616043, 0);
+    tim::vx::TensorSpec relu_58_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_58_out0_quant);
+    auto relu_58_out0 = graph->CreateTensor(relu_58_out0_spec);
+
+    tim::vx::Quantization convolution_59_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.10206783562898636, 139);
+    tim::vx::TensorSpec convolution_59_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_59_out0_quant);
+    auto convolution_59_out0 = graph->CreateTensor(convolution_59_out0_spec);
+
+    tim::vx::ShapeType convolution_59_weight_shape({1,1,128,512});
+    tim::vx::Quantization convolution_59_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.01444157399237156, 121);
+    tim::vx::TensorSpec convolution_59_weight_spec(tim::vx::DataType::UINT8, convolution_59_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_59_weight_quant);
+    auto convolution_59_weight = graph->CreateTensor(convolution_59_weight_spec, coef_data_ptr + 2595680);
+
+    tim::vx::ShapeType convolution_59_bias_shape({512});
+    tim::vx::Quantization convolution_59_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00035213876981288195, 0);
+    tim::vx::TensorSpec convolution_59_bias_spec(tim::vx::DataType::INT32, convolution_59_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_59_bias_quant);
+    auto convolution_59_bias = graph->CreateTensor(convolution_59_bias_spec, coef_data_ptr + 2593632);
+
+    tim::vx::Quantization add_62_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0565003827214241, 0);
+    tim::vx::TensorSpec add_62_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_62_out0_quant);
+    auto add_62_out0 = graph->CreateTensor(add_62_out0_spec);
+
+    tim::vx::Quantization relu_63_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0565003827214241, 0);
+    tim::vx::TensorSpec relu_63_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_63_out0_quant);
+    auto relu_63_out0 = graph->CreateTensor(relu_63_out0_spec);
+
+    tim::vx::Quantization convolution_64_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.034951645880937576, 0);
+    tim::vx::TensorSpec convolution_64_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_64_out0_quant);
+    auto convolution_64_out0 = graph->CreateTensor(convolution_64_out0_spec);
+
+    tim::vx::ShapeType convolution_64_weight_shape({1,1,512,128});
+    tim::vx::Quantization convolution_64_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0016764937900006771, 102);
+    tim::vx::TensorSpec convolution_64_weight_spec(tim::vx::DataType::UINT8, convolution_64_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_64_weight_quant);
+    auto convolution_64_weight = graph->CreateTensor(convolution_64_weight_spec, coef_data_ptr + 2661728);
+
+    tim::vx::ShapeType convolution_64_bias_shape({128});
+    tim::vx::Quantization convolution_64_bias_quant(tim::vx::QuantType::ASYMMETRIC, 9.472254168940708e-05, 0);
+    tim::vx::TensorSpec convolution_64_bias_spec(tim::vx::DataType::INT32, convolution_64_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_64_bias_quant);
+    auto convolution_64_bias = graph->CreateTensor(convolution_64_bias_spec, coef_data_ptr + 2661216);
+
+    tim::vx::Quantization relu_67_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.034951645880937576, 0);
+    tim::vx::TensorSpec relu_67_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_67_out0_quant);
+    auto relu_67_out0 = graph->CreateTensor(relu_67_out0_spec);
+
+    tim::vx::Quantization convolution_68_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03728686273097992, 0);
+    tim::vx::TensorSpec convolution_68_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_68_out0_quant);
+    auto convolution_68_out0 = graph->CreateTensor(convolution_68_out0_spec);
+
+    tim::vx::ShapeType convolution_68_weight_shape({3,3,128,128});
+    tim::vx::Quantization convolution_68_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0028692998457700014, 112);
+    tim::vx::TensorSpec convolution_68_weight_spec(tim::vx::DataType::UINT8, convolution_68_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_68_weight_quant);
+    auto convolution_68_weight = graph->CreateTensor(convolution_68_weight_spec, coef_data_ptr + 2727776);
+
+    tim::vx::ShapeType convolution_68_bias_shape({128});
+    tim::vx::Quantization convolution_68_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001002867502393201, 0);
+    tim::vx::TensorSpec convolution_68_bias_spec(tim::vx::DataType::INT32, convolution_68_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_68_bias_quant);
+    auto convolution_68_bias = graph->CreateTensor(convolution_68_bias_spec, coef_data_ptr + 2727264);
+
+    tim::vx::Quantization relu_71_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03728686273097992, 0);
+    tim::vx::TensorSpec relu_71_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_71_out0_quant);
+    auto relu_71_out0 = graph->CreateTensor(relu_71_out0_spec);
+
+    tim::vx::Quantization convolution_72_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0714351087808609, 145);
+    tim::vx::TensorSpec convolution_72_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_72_out0_quant);
+    auto convolution_72_out0 = graph->CreateTensor(convolution_72_out0_spec);
+
+    tim::vx::ShapeType convolution_72_weight_shape({1,1,128,512});
+    tim::vx::Quantization convolution_72_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.014552710577845573, 98);
+    tim::vx::TensorSpec convolution_72_weight_spec(tim::vx::DataType::UINT8, convolution_72_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_72_weight_quant);
+    auto convolution_72_weight = graph->CreateTensor(convolution_72_weight_spec, coef_data_ptr + 2877280);
+
+    tim::vx::ShapeType convolution_72_bias_shape({512});
+    tim::vx::Quantization convolution_72_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0005426249117590487, 0);
+    tim::vx::TensorSpec convolution_72_bias_spec(tim::vx::DataType::INT32, convolution_72_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_72_bias_quant);
+    auto convolution_72_bias = graph->CreateTensor(convolution_72_bias_spec, coef_data_ptr + 2875232);
+
+    tim::vx::Quantization add_75_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06655561923980713, 0);
+    tim::vx::TensorSpec add_75_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_75_out0_quant);
+    auto add_75_out0 = graph->CreateTensor(add_75_out0_spec);
+
+    tim::vx::Quantization relu_76_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06655561923980713, 0);
+    tim::vx::TensorSpec relu_76_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_76_out0_quant);
+    auto relu_76_out0 = graph->CreateTensor(relu_76_out0_spec);
+
+    tim::vx::Quantization convolution_77_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.02692987024784088, 0);
+    tim::vx::TensorSpec convolution_77_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_77_out0_quant);
+    auto convolution_77_out0 = graph->CreateTensor(convolution_77_out0_spec);
+
+    tim::vx::ShapeType convolution_77_weight_shape({1,1,512,128});
+    tim::vx::Quantization convolution_77_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.004116216208785772, 39);
+    tim::vx::TensorSpec convolution_77_weight_spec(tim::vx::DataType::UINT8, convolution_77_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_77_weight_quant);
+    auto convolution_77_weight = graph->CreateTensor(convolution_77_weight_spec, coef_data_ptr + 2943328);
+
+    tim::vx::ShapeType convolution_77_bias_shape({128});
+    tim::vx::Quantization convolution_77_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00027395732467994094, 0);
+    tim::vx::TensorSpec convolution_77_bias_spec(tim::vx::DataType::INT32, convolution_77_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_77_bias_quant);
+    auto convolution_77_bias = graph->CreateTensor(convolution_77_bias_spec, coef_data_ptr + 2942816);
+
+    tim::vx::Quantization relu_80_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.02692987024784088, 0);
+    tim::vx::TensorSpec relu_80_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_80_out0_quant);
+    auto relu_80_out0 = graph->CreateTensor(relu_80_out0_spec);
+
+    tim::vx::Quantization convolution_81_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.040409915149211884, 0);
+    tim::vx::TensorSpec convolution_81_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_81_out0_quant);
+    auto convolution_81_out0 = graph->CreateTensor(convolution_81_out0_spec);
+
+    tim::vx::ShapeType convolution_81_weight_shape({3,3,128,128});
+    tim::vx::Quantization convolution_81_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.008588027209043503, 87);
+    tim::vx::TensorSpec convolution_81_weight_spec(tim::vx::DataType::UINT8, convolution_81_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_81_weight_quant);
+    auto convolution_81_weight = graph->CreateTensor(convolution_81_weight_spec, coef_data_ptr + 3009376);
+
+    tim::vx::ShapeType convolution_81_bias_shape({128});
+    tim::vx::Quantization convolution_81_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00023127446183934808, 0);
+    tim::vx::TensorSpec convolution_81_bias_spec(tim::vx::DataType::INT32, convolution_81_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_81_bias_quant);
+    auto convolution_81_bias = graph->CreateTensor(convolution_81_bias_spec, coef_data_ptr + 3008864);
+
+    tim::vx::Quantization relu_84_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.040409915149211884, 0);
+    tim::vx::TensorSpec relu_84_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_84_out0_quant);
+    auto relu_84_out0 = graph->CreateTensor(relu_84_out0_spec);
+
+    tim::vx::Quantization convolution_85_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.07892583310604095, 145);
+    tim::vx::TensorSpec convolution_85_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_85_out0_quant);
+    auto convolution_85_out0 = graph->CreateTensor(convolution_85_out0_spec);
+
+    tim::vx::ShapeType convolution_85_weight_shape({1,1,128,512});
+    tim::vx::Quantization convolution_85_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.01371678151190281, 140);
+    tim::vx::TensorSpec convolution_85_weight_spec(tim::vx::DataType::UINT8, convolution_85_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_85_weight_quant);
+    auto convolution_85_weight = graph->CreateTensor(convolution_85_weight_spec, coef_data_ptr + 3158880);
+
+    tim::vx::ShapeType convolution_85_bias_shape({512});
+    tim::vx::Quantization convolution_85_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0005542939761653543, 0);
+    tim::vx::TensorSpec convolution_85_bias_spec(tim::vx::DataType::INT32, convolution_85_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_85_bias_quant);
+    auto convolution_85_bias = graph->CreateTensor(convolution_85_bias_spec, coef_data_ptr + 3156832);
+
+    tim::vx::Quantization add_88_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06780294328927994, 0);
+    tim::vx::TensorSpec add_88_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_88_out0_quant);
+    auto add_88_out0 = graph->CreateTensor(add_88_out0_spec);
+
+    tim::vx::Quantization relu_89_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06780294328927994, 0);
+    tim::vx::TensorSpec relu_89_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_89_out0_quant);
+    auto relu_89_out0 = graph->CreateTensor(relu_89_out0_spec);
+
+    tim::vx::Quantization convolution_90_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.039215199649333954, 0);
+    tim::vx::TensorSpec convolution_90_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_90_out0_quant);
+    auto convolution_90_out0 = graph->CreateTensor(convolution_90_out0_spec);
+
+    tim::vx::ShapeType convolution_90_weight_shape({1,1,512,128});
+    tim::vx::Quantization convolution_90_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.003800247795879841, 116);
+    tim::vx::TensorSpec convolution_90_weight_spec(tim::vx::DataType::UINT8, convolution_90_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_90_weight_quant);
+    auto convolution_90_weight = graph->CreateTensor(convolution_90_weight_spec, coef_data_ptr + 3224928);
+
+    tim::vx::ShapeType convolution_90_bias_shape({128});
+    tim::vx::Quantization convolution_90_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00025766799808479846, 0);
+    tim::vx::TensorSpec convolution_90_bias_spec(tim::vx::DataType::INT32, convolution_90_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_90_bias_quant);
+    auto convolution_90_bias = graph->CreateTensor(convolution_90_bias_spec, coef_data_ptr + 3224416);
+
+    tim::vx::Quantization relu_93_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.039215199649333954, 0);
+    tim::vx::TensorSpec relu_93_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_93_out0_quant);
+    auto relu_93_out0 = graph->CreateTensor(relu_93_out0_spec);
+
+    tim::vx::Quantization convolution_94_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03448693826794624, 0);
+    tim::vx::TensorSpec convolution_94_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_94_out0_quant);
+    auto convolution_94_out0 = graph->CreateTensor(convolution_94_out0_spec);
+
+    tim::vx::ShapeType convolution_94_weight_shape({3,3,128,128});
+    tim::vx::Quantization convolution_94_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.002774078631773591, 109);
+    tim::vx::TensorSpec convolution_94_weight_spec(tim::vx::DataType::UINT8, convolution_94_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_94_weight_quant);
+    auto convolution_94_weight = graph->CreateTensor(convolution_94_weight_spec, coef_data_ptr + 3290976);
+
+    tim::vx::ShapeType convolution_94_bias_shape({128});
+    tim::vx::Quantization convolution_94_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00010878605098696426, 0);
+    tim::vx::TensorSpec convolution_94_bias_spec(tim::vx::DataType::INT32, convolution_94_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_94_bias_quant);
+    auto convolution_94_bias = graph->CreateTensor(convolution_94_bias_spec, coef_data_ptr + 3290464);
+
+    tim::vx::Quantization relu_97_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.03448693826794624, 0);
+    tim::vx::TensorSpec relu_97_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_97_out0_quant);
+    auto relu_97_out0 = graph->CreateTensor(relu_97_out0_spec);
+
+    tim::vx::Quantization convolution_98_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.07904094457626343, 120);
+    tim::vx::TensorSpec convolution_98_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_98_out0_quant);
+    auto convolution_98_out0 = graph->CreateTensor(convolution_98_out0_spec);
+
+    tim::vx::ShapeType convolution_98_weight_shape({1,1,128,512});
+    tim::vx::Quantization convolution_98_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.008222552947700024, 119);
+    tim::vx::TensorSpec convolution_98_weight_spec(tim::vx::DataType::UINT8, convolution_98_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_98_weight_quant);
+    auto convolution_98_weight = graph->CreateTensor(convolution_98_weight_spec, coef_data_ptr + 3440480);
+
+    tim::vx::ShapeType convolution_98_bias_shape({512});
+    tim::vx::Quantization convolution_98_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00028357066912576556, 0);
+    tim::vx::TensorSpec convolution_98_bias_spec(tim::vx::DataType::INT32, convolution_98_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_98_bias_quant);
+    auto convolution_98_bias = graph->CreateTensor(convolution_98_bias_spec, coef_data_ptr + 3438432);
+
+    tim::vx::Quantization add_101_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06775879859924316, 0);
+    tim::vx::TensorSpec add_101_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_101_out0_quant);
+    auto add_101_out0 = graph->CreateTensor(add_101_out0_spec);
+
+    tim::vx::Quantization relu_102_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06775879859924316, 0);
+    tim::vx::TensorSpec relu_102_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_102_out0_quant);
+    auto relu_102_out0 = graph->CreateTensor(relu_102_out0_spec);
+
+    tim::vx::Quantization convolution_103_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.09388317167758942, 137);
+    tim::vx::TensorSpec convolution_103_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_103_out0_quant);
+    auto convolution_103_out0 = graph->CreateTensor(convolution_103_out0_spec);
+
+    tim::vx::ShapeType convolution_103_weight_shape({1,1,512,1024});
+    tim::vx::Quantization convolution_103_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.004281576722860336, 116);
+    tim::vx::TensorSpec convolution_103_weight_spec(tim::vx::DataType::UINT8, convolution_103_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_103_weight_quant);
+    auto convolution_103_weight = graph->CreateTensor(convolution_103_weight_spec, coef_data_ptr + 3510112);
+
+    tim::vx::ShapeType convolution_103_bias_shape({1024});
+    tim::vx::Quantization convolution_103_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0002901144907809794, 0);
+    tim::vx::TensorSpec convolution_103_bias_spec(tim::vx::DataType::INT32, convolution_103_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_103_bias_quant);
+    auto convolution_103_bias = graph->CreateTensor(convolution_103_bias_spec, coef_data_ptr + 3506016);
+
+    tim::vx::Quantization convolution_106_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.035920802503824234, 0);
+    tim::vx::TensorSpec convolution_106_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_106_out0_quant);
+    auto convolution_106_out0 = graph->CreateTensor(convolution_106_out0_spec);
+
+    tim::vx::ShapeType convolution_106_weight_shape({1,1,512,256});
+    tim::vx::Quantization convolution_106_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0029459758661687374, 86);
+    tim::vx::TensorSpec convolution_106_weight_spec(tim::vx::DataType::UINT8, convolution_106_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_106_weight_quant);
+    auto convolution_106_weight = graph->CreateTensor(convolution_106_weight_spec, coef_data_ptr + 4035424);
+
+    tim::vx::ShapeType convolution_106_bias_shape({256});
+    tim::vx::Quantization convolution_106_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00019961578072980046, 0);
+    tim::vx::TensorSpec convolution_106_bias_spec(tim::vx::DataType::INT32, convolution_106_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_106_bias_quant);
+    auto convolution_106_bias = graph->CreateTensor(convolution_106_bias_spec, coef_data_ptr + 4034400);
+
+    tim::vx::Quantization relu_109_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.035920802503824234, 0);
+    tim::vx::TensorSpec relu_109_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_109_out0_quant);
+    auto relu_109_out0 = graph->CreateTensor(relu_109_out0_spec);
+
+    tim::vx::Quantization convolution_110_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.029010910540819168, 0);
+    tim::vx::TensorSpec convolution_110_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_110_out0_quant);
+    auto convolution_110_out0 = graph->CreateTensor(convolution_110_out0_spec);
+
+    tim::vx::ShapeType convolution_110_weight_shape({3,3,256,256});
+    tim::vx::Quantization convolution_110_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0018829640466719866, 113);
+    tim::vx::TensorSpec convolution_110_weight_spec(tim::vx::DataType::UINT8, convolution_110_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_110_weight_quant);
+    auto convolution_110_weight = graph->CreateTensor(convolution_110_weight_spec, coef_data_ptr + 4167520);
+
+    tim::vx::ShapeType convolution_110_bias_shape({256});
+    tim::vx::Quantization convolution_110_bias_quant(tim::vx::QuantType::ASYMMETRIC, 6.763757846783847e-05, 0);
+    tim::vx::TensorSpec convolution_110_bias_spec(tim::vx::DataType::INT32, convolution_110_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_110_bias_quant);
+    auto convolution_110_bias = graph->CreateTensor(convolution_110_bias_spec, coef_data_ptr + 4166496);
+
+    tim::vx::Quantization relu_113_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.029010910540819168, 0);
+    tim::vx::TensorSpec relu_113_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_113_out0_quant);
+    auto relu_113_out0 = graph->CreateTensor(relu_113_out0_spec);
+
+    tim::vx::Quantization convolution_114_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0766356959939003, 137);
+    tim::vx::TensorSpec convolution_114_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_114_out0_quant);
+    auto convolution_114_out0 = graph->CreateTensor(convolution_114_out0_spec);
+
+    tim::vx::ShapeType convolution_114_weight_shape({1,1,256,1024});
+    tim::vx::Quantization convolution_114_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.012337024323642254, 136);
+    tim::vx::TensorSpec convolution_114_weight_spec(tim::vx::DataType::UINT8, convolution_114_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_114_weight_quant);
+    auto convolution_114_weight = graph->CreateTensor(convolution_114_weight_spec, coef_data_ptr + 4761440);
+
+    tim::vx::ShapeType convolution_114_bias_shape({1024});
+    tim::vx::Quantization convolution_114_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00035790831316262484, 0);
+    tim::vx::TensorSpec convolution_114_bias_spec(tim::vx::DataType::INT32, convolution_114_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_114_bias_quant);
+    auto convolution_114_bias = graph->CreateTensor(convolution_114_bias_spec, coef_data_ptr + 4757344);
+
+    tim::vx::Quantization add_117_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05127289146184921, 0);
+    tim::vx::TensorSpec add_117_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_117_out0_quant);
+    auto add_117_out0 = graph->CreateTensor(add_117_out0_spec);
+
+    tim::vx::Quantization relu_118_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05127289146184921, 0);
+    tim::vx::TensorSpec relu_118_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_118_out0_quant);
+    auto relu_118_out0 = graph->CreateTensor(relu_118_out0_spec);
+
+    tim::vx::Quantization convolution_119_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.021251065656542778, 0);
+    tim::vx::TensorSpec convolution_119_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_119_out0_quant);
+    auto convolution_119_out0 = graph->CreateTensor(convolution_119_out0_spec);
+
+    tim::vx::ShapeType convolution_119_weight_shape({1,1,1024,256});
+    tim::vx::Quantization convolution_119_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0021608914248645306, 74);
+    tim::vx::TensorSpec convolution_119_weight_spec(tim::vx::DataType::UINT8, convolution_119_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_119_weight_quant);
+    auto convolution_119_weight = graph->CreateTensor(convolution_119_weight_spec, coef_data_ptr + 5024608);
+
+    tim::vx::ShapeType convolution_119_bias_shape({256});
+    tim::vx::Quantization convolution_119_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011079515388701111, 0);
+    tim::vx::TensorSpec convolution_119_bias_spec(tim::vx::DataType::INT32, convolution_119_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_119_bias_quant);
+    auto convolution_119_bias = graph->CreateTensor(convolution_119_bias_spec, coef_data_ptr + 5023584);
+
+    tim::vx::Quantization relu_122_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.021251065656542778, 0);
+    tim::vx::TensorSpec relu_122_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_122_out0_quant);
+    auto relu_122_out0 = graph->CreateTensor(relu_122_out0_spec);
+
+    tim::vx::Quantization convolution_123_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.017823796719312668, 0);
+    tim::vx::TensorSpec convolution_123_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_123_out0_quant);
+    auto convolution_123_out0 = graph->CreateTensor(convolution_123_out0_spec);
+
+    tim::vx::ShapeType convolution_123_weight_shape({3,3,256,256});
+    tim::vx::Quantization convolution_123_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.006218116730451584, 100);
+    tim::vx::TensorSpec convolution_123_weight_spec(tim::vx::DataType::UINT8, convolution_123_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_123_weight_quant);
+    auto convolution_123_weight = graph->CreateTensor(convolution_123_weight_spec, coef_data_ptr + 5287776);
+
+    tim::vx::ShapeType convolution_123_bias_shape({256});
+    tim::vx::Quantization convolution_123_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00013214160571806133, 0);
+    tim::vx::TensorSpec convolution_123_bias_spec(tim::vx::DataType::INT32, convolution_123_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_123_bias_quant);
+    auto convolution_123_bias = graph->CreateTensor(convolution_123_bias_spec, coef_data_ptr + 5286752);
+
+    tim::vx::Quantization relu_126_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.017823796719312668, 0);
+    tim::vx::TensorSpec relu_126_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_126_out0_quant);
+    auto relu_126_out0 = graph->CreateTensor(relu_126_out0_spec);
+
+    tim::vx::Quantization convolution_127_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.056391555815935135, 108);
+    tim::vx::TensorSpec convolution_127_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_127_out0_quant);
+    auto convolution_127_out0 = graph->CreateTensor(convolution_127_out0_spec);
+
+    tim::vx::ShapeType convolution_127_weight_shape({1,1,256,1024});
+    tim::vx::Quantization convolution_127_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.013343931175768375, 141);
+    tim::vx::TensorSpec convolution_127_weight_spec(tim::vx::DataType::UINT8, convolution_127_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_127_weight_quant);
+    auto convolution_127_weight = graph->CreateTensor(convolution_127_weight_spec, coef_data_ptr + 5881696);
+
+    tim::vx::ShapeType convolution_127_bias_shape({1024});
+    tim::vx::Quantization convolution_127_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0002378395147388801, 0);
+    tim::vx::TensorSpec convolution_127_bias_spec(tim::vx::DataType::INT32, convolution_127_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_127_bias_quant);
+    auto convolution_127_bias = graph->CreateTensor(convolution_127_bias_spec, coef_data_ptr + 5877600);
+
+    tim::vx::Quantization add_130_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05008764564990997, 0);
+    tim::vx::TensorSpec add_130_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_130_out0_quant);
+    auto add_130_out0 = graph->CreateTensor(add_130_out0_spec);
+
+    tim::vx::Quantization relu_131_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05008764564990997, 0);
+    tim::vx::TensorSpec relu_131_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_131_out0_quant);
+    auto relu_131_out0 = graph->CreateTensor(relu_131_out0_spec);
+
+    tim::vx::Quantization convolution_132_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.020675403997302055, 0);
+    tim::vx::TensorSpec convolution_132_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_132_out0_quant);
+    auto convolution_132_out0 = graph->CreateTensor(convolution_132_out0_spec);
+
+    tim::vx::ShapeType convolution_132_weight_shape({1,1,1024,256});
+    tim::vx::Quantization convolution_132_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.002604473615065217, 132);
+    tim::vx::TensorSpec convolution_132_weight_spec(tim::vx::DataType::UINT8, convolution_132_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_132_weight_quant);
+    auto convolution_132_weight = graph->CreateTensor(convolution_132_weight_spec, coef_data_ptr + 6144864);
+
+    tim::vx::ShapeType convolution_132_bias_shape({256});
+    tim::vx::Quantization convolution_132_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001304519537370652, 0);
+    tim::vx::TensorSpec convolution_132_bias_spec(tim::vx::DataType::INT32, convolution_132_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_132_bias_quant);
+    auto convolution_132_bias = graph->CreateTensor(convolution_132_bias_spec, coef_data_ptr + 6143840);
+
+    tim::vx::Quantization relu_135_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.020675403997302055, 0);
+    tim::vx::TensorSpec relu_135_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_135_out0_quant);
+    auto relu_135_out0 = graph->CreateTensor(relu_135_out0_spec);
+
+    tim::vx::Quantization convolution_136_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.02770310267806053, 0);
+    tim::vx::TensorSpec convolution_136_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_136_out0_quant);
+    auto convolution_136_out0 = graph->CreateTensor(convolution_136_out0_spec);
+
+    tim::vx::ShapeType convolution_136_weight_shape({3,3,256,256});
+    tim::vx::Quantization convolution_136_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.005561390426009893, 91);
+    tim::vx::TensorSpec convolution_136_weight_spec(tim::vx::DataType::UINT8, convolution_136_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_136_weight_quant);
+    auto convolution_136_weight = graph->CreateTensor(convolution_136_weight_spec, coef_data_ptr + 6408032);
+
+    tim::vx::ShapeType convolution_136_bias_shape({256});
+    tim::vx::Quantization convolution_136_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011498399544507265, 0);
+    tim::vx::TensorSpec convolution_136_bias_spec(tim::vx::DataType::INT32, convolution_136_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_136_bias_quant);
+    auto convolution_136_bias = graph->CreateTensor(convolution_136_bias_spec, coef_data_ptr + 6407008);
+
+    tim::vx::Quantization relu_139_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.02770310267806053, 0);
+    tim::vx::TensorSpec relu_139_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_139_out0_quant);
+    auto relu_139_out0 = graph->CreateTensor(relu_139_out0_spec);
+
+    tim::vx::Quantization convolution_140_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.050987932831048965, 122);
+    tim::vx::TensorSpec convolution_140_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_140_out0_quant);
+    auto convolution_140_out0 = graph->CreateTensor(convolution_140_out0_spec);
+
+    tim::vx::ShapeType convolution_140_weight_shape({1,1,256,1024});
+    tim::vx::Quantization convolution_140_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.009768206626176834, 86);
+    tim::vx::TensorSpec convolution_140_weight_spec(tim::vx::DataType::UINT8, convolution_140_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_140_weight_quant);
+    auto convolution_140_weight = graph->CreateTensor(convolution_140_weight_spec, coef_data_ptr + 7001952);
+
+    tim::vx::ShapeType convolution_140_bias_shape({1024});
+    tim::vx::Quantization convolution_140_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0002706096274778247, 0);
+    tim::vx::TensorSpec convolution_140_bias_spec(tim::vx::DataType::INT32, convolution_140_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_140_bias_quant);
+    auto convolution_140_bias = graph->CreateTensor(convolution_140_bias_spec, coef_data_ptr + 6997856);
+
+    tim::vx::Quantization add_143_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06169896572828293, 0);
+    tim::vx::TensorSpec add_143_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_143_out0_quant);
+    auto add_143_out0 = graph->CreateTensor(add_143_out0_spec);
+
+    tim::vx::Quantization relu_144_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.06169896572828293, 0);
+    tim::vx::TensorSpec relu_144_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_144_out0_quant);
+    auto relu_144_out0 = graph->CreateTensor(relu_144_out0_spec);
+
+    tim::vx::Quantization convolution_145_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.01976320892572403, 0);
+    tim::vx::TensorSpec convolution_145_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_145_out0_quant);
+    auto convolution_145_out0 = graph->CreateTensor(convolution_145_out0_spec);
+
+    tim::vx::ShapeType convolution_145_weight_shape({1,1,1024,256});
+    tim::vx::Quantization convolution_145_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0022279482800513506, 72);
+    tim::vx::TensorSpec convolution_145_weight_spec(tim::vx::DataType::UINT8, convolution_145_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_145_weight_quant);
+    auto convolution_145_weight = graph->CreateTensor(convolution_145_weight_spec, coef_data_ptr + 7265120);
+
+    tim::vx::ShapeType convolution_145_bias_shape({256});
+    tim::vx::Quantization convolution_145_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00013746210606768727, 0);
+    tim::vx::TensorSpec convolution_145_bias_spec(tim::vx::DataType::INT32, convolution_145_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_145_bias_quant);
+    auto convolution_145_bias = graph->CreateTensor(convolution_145_bias_spec, coef_data_ptr + 7264096);
+
+    tim::vx::Quantization relu_148_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.01976320892572403, 0);
+    tim::vx::TensorSpec relu_148_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_148_out0_quant);
+    auto relu_148_out0 = graph->CreateTensor(relu_148_out0_spec);
+
+    tim::vx::Quantization convolution_149_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023187527433037758, 0);
+    tim::vx::TensorSpec convolution_149_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_149_out0_quant);
+    auto convolution_149_out0 = graph->CreateTensor(convolution_149_out0_spec);
+
+    tim::vx::ShapeType convolution_149_weight_shape({3,3,256,256});
+    tim::vx::Quantization convolution_149_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.004769242834299803, 123);
+    tim::vx::TensorSpec convolution_149_weight_spec(tim::vx::DataType::UINT8, convolution_149_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_149_weight_quant);
+    auto convolution_149_weight = graph->CreateTensor(convolution_149_weight_spec, coef_data_ptr + 7528288);
+
+    tim::vx::ShapeType convolution_149_bias_shape({256});
+    tim::vx::Quantization convolution_149_bias_quant(tim::vx::QuantType::ASYMMETRIC, 9.425554162589833e-05, 0);
+    tim::vx::TensorSpec convolution_149_bias_spec(tim::vx::DataType::INT32, convolution_149_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_149_bias_quant);
+    auto convolution_149_bias = graph->CreateTensor(convolution_149_bias_spec, coef_data_ptr + 7527264);
+
+    tim::vx::Quantization relu_152_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.023187527433037758, 0);
+    tim::vx::TensorSpec relu_152_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_152_out0_quant);
+    auto relu_152_out0 = graph->CreateTensor(relu_152_out0_spec);
+
+    tim::vx::Quantization convolution_153_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.057725995779037476, 120);
+    tim::vx::TensorSpec convolution_153_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_153_out0_quant);
+    auto convolution_153_out0 = graph->CreateTensor(convolution_153_out0_spec);
+
+    tim::vx::ShapeType convolution_153_weight_shape({1,1,256,1024});
+    tim::vx::Quantization convolution_153_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.01152096502482891, 112);
+    tim::vx::TensorSpec convolution_153_weight_spec(tim::vx::DataType::UINT8, convolution_153_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_153_weight_quant);
+    auto convolution_153_weight = graph->CreateTensor(convolution_153_weight_spec, coef_data_ptr + 8122208);
+
+    tim::vx::ShapeType convolution_153_bias_shape({1024});
+    tim::vx::Quantization convolution_153_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0002671426918823272, 0);
+    tim::vx::TensorSpec convolution_153_bias_spec(tim::vx::DataType::INT32, convolution_153_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_153_bias_quant);
+    auto convolution_153_bias = graph->CreateTensor(convolution_153_bias_spec, coef_data_ptr + 8118112);
+
+    tim::vx::Quantization add_156_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0601431243121624, 0);
+    tim::vx::TensorSpec add_156_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_156_out0_quant);
+    auto add_156_out0 = graph->CreateTensor(add_156_out0_spec);
+
+    tim::vx::Quantization relu_157_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.0601431243121624, 0);
+    tim::vx::TensorSpec relu_157_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_157_out0_quant);
+    auto relu_157_out0 = graph->CreateTensor(relu_157_out0_spec);
+
+    tim::vx::Quantization convolution_158_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.019502228125929832, 0);
+    tim::vx::TensorSpec convolution_158_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_158_out0_quant);
+    auto convolution_158_out0 = graph->CreateTensor(convolution_158_out0_spec);
+
+    tim::vx::ShapeType convolution_158_weight_shape({1,1,1024,256});
+    tim::vx::Quantization convolution_158_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0024551330134272575, 79);
+    tim::vx::TensorSpec convolution_158_weight_spec(tim::vx::DataType::UINT8, convolution_158_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_158_weight_quant);
+    auto convolution_158_weight = graph->CreateTensor(convolution_158_weight_spec, coef_data_ptr + 8385376);
+
+    tim::vx::ShapeType convolution_158_bias_shape({256});
+    tim::vx::Quantization convolution_158_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00014765937521588057, 0);
+    tim::vx::TensorSpec convolution_158_bias_spec(tim::vx::DataType::INT32, convolution_158_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_158_bias_quant);
+    auto convolution_158_bias = graph->CreateTensor(convolution_158_bias_spec, coef_data_ptr + 8384352);
+
+    tim::vx::Quantization relu_161_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.019502228125929832, 0);
+    tim::vx::TensorSpec relu_161_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_161_out0_quant);
+    auto relu_161_out0 = graph->CreateTensor(relu_161_out0_spec);
+
+    tim::vx::Quantization convolution_162_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.019909709692001343, 0);
+    tim::vx::TensorSpec convolution_162_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_162_out0_quant);
+    auto convolution_162_out0 = graph->CreateTensor(convolution_162_out0_spec);
+
+    tim::vx::ShapeType convolution_162_weight_shape({3,3,256,256});
+    tim::vx::Quantization convolution_162_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0050792936235666275, 120);
+    tim::vx::TensorSpec convolution_162_weight_spec(tim::vx::DataType::UINT8, convolution_162_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_162_weight_quant);
+    auto convolution_162_weight = graph->CreateTensor(convolution_162_weight_spec, coef_data_ptr + 8648544);
+
+    tim::vx::ShapeType convolution_162_bias_shape({256});
+    tim::vx::Quantization convolution_162_bias_quant(tim::vx::QuantType::ASYMMETRIC, 9.905754268402234e-05, 0);
+    tim::vx::TensorSpec convolution_162_bias_spec(tim::vx::DataType::INT32, convolution_162_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_162_bias_quant);
+    auto convolution_162_bias = graph->CreateTensor(convolution_162_bias_spec, coef_data_ptr + 8647520);
+
+    tim::vx::Quantization relu_165_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.019909709692001343, 0);
+    tim::vx::TensorSpec relu_165_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_165_out0_quant);
+    auto relu_165_out0 = graph->CreateTensor(relu_165_out0_spec);
+
+    tim::vx::Quantization convolution_166_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.04841376096010208, 118);
+    tim::vx::TensorSpec convolution_166_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_166_out0_quant);
+    auto convolution_166_out0 = graph->CreateTensor(convolution_166_out0_spec);
+
+    tim::vx::ShapeType convolution_166_weight_shape({1,1,256,1024});
+    tim::vx::Quantization convolution_166_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.008337733335793018, 101);
+    tim::vx::TensorSpec convolution_166_weight_spec(tim::vx::DataType::UINT8, convolution_166_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_166_weight_quant);
+    auto convolution_166_weight = graph->CreateTensor(convolution_166_weight_spec, coef_data_ptr + 9242464);
+
+    tim::vx::ShapeType convolution_166_bias_shape({1024});
+    tim::vx::Quantization convolution_166_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00016600184608250856, 0);
+    tim::vx::TensorSpec convolution_166_bias_spec(tim::vx::DataType::INT32, convolution_166_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_166_bias_quant);
+    auto convolution_166_bias = graph->CreateTensor(convolution_166_bias_spec, coef_data_ptr + 9238368);
+
+    tim::vx::Quantization add_169_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05369938164949417, 0);
+    tim::vx::TensorSpec add_169_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_169_out0_quant);
+    auto add_169_out0 = graph->CreateTensor(add_169_out0_spec);
+
+    tim::vx::Quantization relu_170_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.05369938164949417, 0);
+    tim::vx::TensorSpec relu_170_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_170_out0_quant);
+    auto relu_170_out0 = graph->CreateTensor(relu_170_out0_spec);
+
+    tim::vx::Quantization convolution_171_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.028263499960303307, 0);
+    tim::vx::TensorSpec convolution_171_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_171_out0_quant);
+    auto convolution_171_out0 = graph->CreateTensor(convolution_171_out0_spec);
+
+    tim::vx::ShapeType convolution_171_weight_shape({1,1,1024,256});
+    tim::vx::Quantization convolution_171_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.002423967467620969, 80);
+    tim::vx::TensorSpec convolution_171_weight_spec(tim::vx::DataType::UINT8, convolution_171_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_171_weight_quant);
+    auto convolution_171_weight = graph->CreateTensor(convolution_171_weight_spec, coef_data_ptr + 9505632);
+
+    tim::vx::ShapeType convolution_171_bias_shape({256});
+    tim::vx::Quantization convolution_171_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0001301655574934557, 0);
+    tim::vx::TensorSpec convolution_171_bias_spec(tim::vx::DataType::INT32, convolution_171_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_171_bias_quant);
+    auto convolution_171_bias = graph->CreateTensor(convolution_171_bias_spec, coef_data_ptr + 9504608);
+
+    tim::vx::Quantization relu_174_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.028263499960303307, 0);
+    tim::vx::TensorSpec relu_174_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_174_out0_quant);
+    auto relu_174_out0 = graph->CreateTensor(relu_174_out0_spec);
+
+    tim::vx::Quantization convolution_175_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.08250340074300766, 0);
+    tim::vx::TensorSpec convolution_175_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_175_out0_quant);
+    auto convolution_175_out0 = graph->CreateTensor(convolution_175_out0_spec);
+
+    tim::vx::ShapeType convolution_175_weight_shape({3,3,256,256});
+    tim::vx::Quantization convolution_175_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.004059859085828066, 125);
+    tim::vx::TensorSpec convolution_175_weight_spec(tim::vx::DataType::UINT8, convolution_175_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_175_weight_quant);
+    auto convolution_175_weight = graph->CreateTensor(convolution_175_weight_spec, coef_data_ptr + 9768800);
+
+    tim::vx::ShapeType convolution_175_bias_shape({256});
+    tim::vx::Quantization convolution_175_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011474582424852997, 0);
+    tim::vx::TensorSpec convolution_175_bias_spec(tim::vx::DataType::INT32, convolution_175_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_175_bias_quant);
+    auto convolution_175_bias = graph->CreateTensor(convolution_175_bias_spec, coef_data_ptr + 9767776);
+
+    tim::vx::Quantization relu_178_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.08250340074300766, 0);
+    tim::vx::TensorSpec relu_178_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_178_out0_quant);
+    auto relu_178_out0 = graph->CreateTensor(relu_178_out0_spec);
+
+    tim::vx::Quantization convolution_179_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.09280265122652054, 87);
+    tim::vx::TensorSpec convolution_179_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_179_out0_quant);
+    auto convolution_179_out0 = graph->CreateTensor(convolution_179_out0_spec);
+
+    tim::vx::ShapeType convolution_179_weight_shape({1,1,256,1024});
+    tim::vx::Quantization convolution_179_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.007674170657992363, 78);
+    tim::vx::TensorSpec convolution_179_weight_spec(tim::vx::DataType::UINT8, convolution_179_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_179_weight_quant);
+    auto convolution_179_weight = graph->CreateTensor(convolution_179_weight_spec, coef_data_ptr + 10362720);
+
+    tim::vx::ShapeType convolution_179_bias_shape({1024});
+    tim::vx::Quantization convolution_179_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0006331451586447656, 0);
+    tim::vx::TensorSpec convolution_179_bias_spec(tim::vx::DataType::INT32, convolution_179_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_179_bias_quant);
+    auto convolution_179_bias = graph->CreateTensor(convolution_179_bias_spec, coef_data_ptr + 10358624);
+
+    tim::vx::Quantization add_182_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.061852194368839264, 0);
+    tim::vx::TensorSpec add_182_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_182_out0_quant);
+    auto add_182_out0 = graph->CreateTensor(add_182_out0_spec);
+
+    tim::vx::Quantization relu_183_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.061852194368839264, 0);
+    tim::vx::TensorSpec relu_183_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_183_out0_quant);
+    auto relu_183_out0 = graph->CreateTensor(relu_183_out0_spec);
+
+    tim::vx::Quantization convolution_184_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.1570115089416504, 102);
+    tim::vx::TensorSpec convolution_184_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_184_out0_quant);
+    auto convolution_184_out0 = graph->CreateTensor(convolution_184_out0_spec);
+
+    tim::vx::ShapeType convolution_184_weight_shape({1,1,1024,2048});
+    tim::vx::Quantization convolution_184_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.021532153710722923, 131);
+    tim::vx::TensorSpec convolution_184_weight_spec(tim::vx::DataType::UINT8, convolution_184_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_184_weight_quant);
+    auto convolution_184_weight = graph->CreateTensor(convolution_184_weight_spec, coef_data_ptr + 10633056);
+
+    tim::vx::ShapeType convolution_184_bias_shape({2048});
+    tim::vx::Quantization convolution_184_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.001331810955889523, 0);
+    tim::vx::TensorSpec convolution_184_bias_spec(tim::vx::DataType::INT32, convolution_184_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_184_bias_quant);
+    auto convolution_184_bias = graph->CreateTensor(convolution_184_bias_spec, coef_data_ptr + 10624864);
+
+    tim::vx::Quantization convolution_187_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.018865292891860008, 0);
+    tim::vx::TensorSpec convolution_187_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_187_out0_quant);
+    auto convolution_187_out0 = graph->CreateTensor(convolution_187_out0_spec);
+
+    tim::vx::ShapeType convolution_187_weight_shape({1,1,1024,512});
+    tim::vx::Quantization convolution_187_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0020486777648329735, 90);
+    tim::vx::TensorSpec convolution_187_weight_spec(tim::vx::DataType::UINT8, convolution_187_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_187_weight_quant);
+    auto convolution_187_weight = graph->CreateTensor(convolution_187_weight_spec, coef_data_ptr + 12732256);
+
+    tim::vx::ShapeType convolution_187_bias_shape({512});
+    tim::vx::Quantization convolution_187_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00012671521108131856, 0);
+    tim::vx::TensorSpec convolution_187_bias_spec(tim::vx::DataType::INT32, convolution_187_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_187_bias_quant);
+    auto convolution_187_bias = graph->CreateTensor(convolution_187_bias_spec, coef_data_ptr + 12730208);
+
+    tim::vx::Quantization relu_190_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.018865292891860008, 0);
+    tim::vx::TensorSpec relu_190_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_190_out0_quant);
+    auto relu_190_out0 = graph->CreateTensor(relu_190_out0_spec);
+
+    tim::vx::Quantization convolution_191_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.01558383647352457, 0);
+    tim::vx::TensorSpec convolution_191_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_191_out0_quant);
+    auto convolution_191_out0 = graph->CreateTensor(convolution_191_out0_spec);
+
+    tim::vx::ShapeType convolution_191_weight_shape({3,3,512,512});
+    tim::vx::Quantization convolution_191_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0030953562818467617, 75);
+    tim::vx::TensorSpec convolution_191_weight_spec(tim::vx::DataType::UINT8, convolution_191_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_191_weight_quant);
+    auto convolution_191_weight = graph->CreateTensor(convolution_191_weight_spec, coef_data_ptr + 13258592);
+
+    tim::vx::ShapeType convolution_191_bias_shape({512});
+    tim::vx::Quantization convolution_191_bias_quant(tim::vx::QuantType::ASYMMETRIC, 5.839480218128301e-05, 0);
+    tim::vx::TensorSpec convolution_191_bias_spec(tim::vx::DataType::INT32, convolution_191_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_191_bias_quant);
+    auto convolution_191_bias = graph->CreateTensor(convolution_191_bias_spec, coef_data_ptr + 13256544);
+
+    tim::vx::Quantization relu_194_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.01558383647352457, 0);
+    tim::vx::TensorSpec relu_194_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_194_out0_quant);
+    auto relu_194_out0 = graph->CreateTensor(relu_194_out0_spec);
+
+    tim::vx::Quantization convolution_195_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.08450666815042496, 120);
+    tim::vx::TensorSpec convolution_195_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_195_out0_quant);
+    auto convolution_195_out0 = graph->CreateTensor(convolution_195_out0_spec);
+
+    tim::vx::ShapeType convolution_195_weight_shape({1,1,512,2048});
+    tim::vx::Quantization convolution_195_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.02880563586950302, 92);
+    tim::vx::TensorSpec convolution_195_weight_spec(tim::vx::DataType::UINT8, convolution_195_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_195_weight_quant);
+    auto convolution_195_weight = graph->CreateTensor(convolution_195_weight_spec, coef_data_ptr + 15626080);
+
+    tim::vx::ShapeType convolution_195_bias_shape({2048});
+    tim::vx::Quantization convolution_195_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00044890231220051646, 0);
+    tim::vx::TensorSpec convolution_195_bias_spec(tim::vx::DataType::INT32, convolution_195_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_195_bias_quant);
+    auto convolution_195_bias = graph->CreateTensor(convolution_195_bias_spec, coef_data_ptr + 15617888);
+
+    tim::vx::Quantization add_198_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.10979654639959335, 0);
+    tim::vx::TensorSpec add_198_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_198_out0_quant);
+    auto add_198_out0 = graph->CreateTensor(add_198_out0_spec);
+
+    tim::vx::Quantization relu_199_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.10979654639959335, 0);
+    tim::vx::TensorSpec relu_199_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_199_out0_quant);
+    auto relu_199_out0 = graph->CreateTensor(relu_199_out0_spec);
+
+    tim::vx::Quantization convolution_200_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.014015702530741692, 0);
+    tim::vx::TensorSpec convolution_200_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_200_out0_quant);
+    auto convolution_200_out0 = graph->CreateTensor(convolution_200_out0_spec);
+
+    tim::vx::ShapeType convolution_200_weight_shape({1,1,2048,512});
+    tim::vx::Quantization convolution_200_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.000780042028054595, 92);
+    tim::vx::TensorSpec convolution_200_weight_spec(tim::vx::DataType::UINT8, convolution_200_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_200_weight_quant);
+    auto convolution_200_weight = graph->CreateTensor(convolution_200_weight_spec, coef_data_ptr + 16676704);
+
+    tim::vx::ShapeType convolution_200_bias_shape({512});
+    tim::vx::Quantization convolution_200_bias_quant(tim::vx::QuantType::ASYMMETRIC, 8.564592280890793e-05, 0);
+    tim::vx::TensorSpec convolution_200_bias_spec(tim::vx::DataType::INT32, convolution_200_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_200_bias_quant);
+    auto convolution_200_bias = graph->CreateTensor(convolution_200_bias_spec, coef_data_ptr + 16674656);
+
+    tim::vx::Quantization relu_203_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.014015702530741692, 0);
+    tim::vx::TensorSpec relu_203_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_203_out0_quant);
+    auto relu_203_out0 = graph->CreateTensor(relu_203_out0_spec);
+
+    tim::vx::Quantization convolution_204_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.013254771009087563, 0);
+    tim::vx::TensorSpec convolution_204_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_204_out0_quant);
+    auto convolution_204_out0 = graph->CreateTensor(convolution_204_out0_spec);
+
+    tim::vx::ShapeType convolution_204_weight_shape({3,3,512,512});
+    tim::vx::Quantization convolution_204_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0051354775205254555, 94);
+    tim::vx::TensorSpec convolution_204_weight_spec(tim::vx::DataType::UINT8, convolution_204_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_204_weight_quant);
+    auto convolution_204_weight = graph->CreateTensor(convolution_204_weight_spec, coef_data_ptr + 17727328);
+
+    tim::vx::ShapeType convolution_204_bias_shape({512});
+    tim::vx::Quantization convolution_204_bias_quant(tim::vx::QuantType::ASYMMETRIC, 7.197732338681817e-05, 0);
+    tim::vx::TensorSpec convolution_204_bias_spec(tim::vx::DataType::INT32, convolution_204_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_204_bias_quant);
+    auto convolution_204_bias = graph->CreateTensor(convolution_204_bias_spec, coef_data_ptr + 17725280);
+
+    tim::vx::Quantization relu_207_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.013254771009087563, 0);
+    tim::vx::TensorSpec relu_207_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_207_out0_quant);
+    auto relu_207_out0 = graph->CreateTensor(relu_207_out0_spec);
+
+    tim::vx::Quantization convolution_208_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.09129555523395538, 118);
+    tim::vx::TensorSpec convolution_208_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_208_out0_quant);
+    auto convolution_208_out0 = graph->CreateTensor(convolution_208_out0_spec);
+
+    tim::vx::ShapeType convolution_208_weight_shape({1,1,512,2048});
+    tim::vx::Quantization convolution_208_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.026294764131307602, 118);
+    tim::vx::TensorSpec convolution_208_weight_spec(tim::vx::DataType::UINT8, convolution_208_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_208_weight_quant);
+    auto convolution_208_weight = graph->CreateTensor(convolution_208_weight_spec, coef_data_ptr + 20094816);
+
+    tim::vx::ShapeType convolution_208_bias_shape({2048});
+    tim::vx::Quantization convolution_208_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0003485310880932957, 0);
+    tim::vx::TensorSpec convolution_208_bias_spec(tim::vx::DataType::INT32, convolution_208_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_208_bias_quant);
+    auto convolution_208_bias = graph->CreateTensor(convolution_208_bias_spec, coef_data_ptr + 20086624);
+
+    tim::vx::Quantization add_211_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.1367546170949936, 0);
+    tim::vx::TensorSpec add_211_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_211_out0_quant);
+    auto add_211_out0 = graph->CreateTensor(add_211_out0_spec);
+
+    tim::vx::Quantization relu_212_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.1367546170949936, 0);
+    tim::vx::TensorSpec relu_212_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_212_out0_quant);
+    auto relu_212_out0 = graph->CreateTensor(relu_212_out0_spec);
+
+    tim::vx::Quantization convolution_213_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.02462535724043846, 0);
+    tim::vx::TensorSpec convolution_213_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_213_out0_quant);
+    auto convolution_213_out0 = graph->CreateTensor(convolution_213_out0_spec);
+
+    tim::vx::ShapeType convolution_213_weight_shape({1,1,2048,512});
+    tim::vx::Quantization convolution_213_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0007146087591536343, 80);
+    tim::vx::TensorSpec convolution_213_weight_spec(tim::vx::DataType::UINT8, convolution_213_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_213_weight_quant);
+    auto convolution_213_weight = graph->CreateTensor(convolution_213_weight_spec, coef_data_ptr + 21145440);
+
+    tim::vx::ShapeType convolution_213_bias_shape({512});
+    tim::vx::Quantization convolution_213_bias_quant(tim::vx::QuantType::ASYMMETRIC, 9.772604971658438e-05, 0);
+    tim::vx::TensorSpec convolution_213_bias_spec(tim::vx::DataType::INT32, convolution_213_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_213_bias_quant);
+    auto convolution_213_bias = graph->CreateTensor(convolution_213_bias_spec, coef_data_ptr + 21143392);
+
+    tim::vx::Quantization relu_216_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.02462535724043846, 0);
+    tim::vx::TensorSpec relu_216_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_216_out0_quant);
+    auto relu_216_out0 = graph->CreateTensor(relu_216_out0_spec);
+
+    tim::vx::Quantization convolution_217_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.024887174367904663, 0);
+    tim::vx::TensorSpec convolution_217_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_217_out0_quant);
+    auto convolution_217_out0 = graph->CreateTensor(convolution_217_out0_spec);
+
+    tim::vx::ShapeType convolution_217_weight_shape({3,3,512,512});
+    tim::vx::Quantization convolution_217_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.0030633925925940275, 126);
+    tim::vx::TensorSpec convolution_217_weight_spec(tim::vx::DataType::UINT8, convolution_217_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_217_weight_quant);
+    auto convolution_217_weight = graph->CreateTensor(convolution_217_weight_spec, coef_data_ptr + 22196064);
+
+    tim::vx::ShapeType convolution_217_bias_shape({512});
+    tim::vx::Quantization convolution_217_bias_quant(tim::vx::QuantType::ASYMMETRIC, 7.543713581981137e-05, 0);
+    tim::vx::TensorSpec convolution_217_bias_spec(tim::vx::DataType::INT32, convolution_217_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_217_bias_quant);
+    auto convolution_217_bias = graph->CreateTensor(convolution_217_bias_spec, coef_data_ptr + 22194016);
+
+    tim::vx::Quantization relu_220_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.024887174367904663, 0);
+    tim::vx::TensorSpec relu_220_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_220_out0_quant);
+    auto relu_220_out0 = graph->CreateTensor(relu_220_out0_spec);
+
+    tim::vx::Quantization convolution_221_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.1402689665555954, 109);
+    tim::vx::TensorSpec convolution_221_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, convolution_221_out0_quant);
+    auto convolution_221_out0 = graph->CreateTensor(convolution_221_out0_spec);
+
+    tim::vx::ShapeType convolution_221_weight_shape({1,1,512,2048});
+    tim::vx::Quantization convolution_221_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.030694643035531044, 77);
+    tim::vx::TensorSpec convolution_221_weight_spec(tim::vx::DataType::UINT8, convolution_221_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_221_weight_quant);
+    auto convolution_221_weight = graph->CreateTensor(convolution_221_weight_spec, coef_data_ptr + 24563552);
+
+    tim::vx::ShapeType convolution_221_bias_shape({2048});
+    tim::vx::Quantization convolution_221_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.0007639029063284397, 0);
+    tim::vx::TensorSpec convolution_221_bias_spec(tim::vx::DataType::INT32, convolution_221_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, convolution_221_bias_quant);
+    auto convolution_221_bias = graph->CreateTensor(convolution_221_bias_spec, coef_data_ptr + 24555360);
+
+    tim::vx::Quantization add_224_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16616259515285492, 0);
+    tim::vx::TensorSpec add_224_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, add_224_out0_quant);
+    auto add_224_out0 = graph->CreateTensor(add_224_out0_spec);
+
+    tim::vx::Quantization relu_225_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.16616259515285492, 0);
+    tim::vx::TensorSpec relu_225_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, relu_225_out0_quant);
+    auto relu_225_out0 = graph->CreateTensor(relu_225_out0_spec);
+
+    tim::vx::Quantization pooling_226_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.030994946137070656, 0);
+    tim::vx::TensorSpec pooling_226_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, pooling_226_out0_quant);
+    auto pooling_226_out0 = graph->CreateTensor(pooling_226_out0_spec);
+
+    tim::vx::Quantization fullconnect_227_out0_quant(tim::vx::QuantType::ASYMMETRIC, 0.13894738256931305, 52);
+    tim::vx::TensorSpec fullconnect_227_out0_spec(tim::vx::DataType::UINT8, {},
+    tim::vx::TensorAttribute::TRANSIENT, fullconnect_227_out0_quant);
+    auto fullconnect_227_out0 = graph->CreateTensor(fullconnect_227_out0_spec);
+
+    tim::vx::ShapeType fullconnect_227_weight_shape({2048,1000});
+    tim::vx::Quantization fullconnect_227_weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.003711833618581295, 57);
+    tim::vx::TensorSpec fullconnect_227_weight_spec(tim::vx::DataType::UINT8, fullconnect_227_weight_shape,
+    tim::vx::TensorAttribute::CONSTANT, fullconnect_227_weight_quant);
+    auto fullconnect_227_weight = graph->CreateTensor(fullconnect_227_weight_spec, coef_data_ptr + 13664);
+
+    tim::vx::ShapeType fullconnect_227_bias_shape({1000});
+    tim::vx::Quantization fullconnect_227_bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.00011504808207973838, 0);
+    tim::vx::TensorSpec fullconnect_227_bias_spec(tim::vx::DataType::INT32, fullconnect_227_bias_shape,
+    tim::vx::TensorAttribute::CONSTANT, fullconnect_227_bias_quant);
+    auto fullconnect_227_bias = graph->CreateTensor(fullconnect_227_bias_spec, coef_data_ptr + 9664);
+
+    tim::vx::ShapeType input_0_shape({224,224,3,1});
+    tim::vx::Quantization input_0_quant(tim::vx::QuantType::ASYMMETRIC, 1.0, 0);
+    tim::vx::TensorSpec input_0_spec(tim::vx::DataType::UINT8, input_0_shape,
+    tim::vx::TensorAttribute::INPUT, input_0_quant);
+    auto input_0 = graph->CreateTensor(input_0_spec);
+
+    tim::vx::ShapeType output_229_shape({1000,1});
+    tim::vx::TensorSpec output_229_spec(tim::vx::DataType::FLOAT16, output_229_shape,
+    tim::vx::TensorAttribute::OUTPUT);
+    auto output_229 = graph->CreateTensor(output_229_spec);
+
+    resnet50::inputs_tensor.push_back(input_0);
+
+    resnet50::outputs_tensor.push_back(output_229);
+
+    auto convolution_1 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({7,7}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({3,3,3,3}),    // pad
+        0);    // multiplier
+
+    auto relu_4 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto pooling_5 = graph->CreateOperation <tim::vx::ops::Pool2d>(
+        tim::vx::PoolType::MAX,    // type
+        std::array<uint32_t, 4>({0,1,0,1}),    // pad
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        tim::vx::RoundType::CEILING);    // round_type
+
+    auto convolution_9 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_12 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_13 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_16 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto concat_231 = graph->CreateOperation <tim::vx::ops::Concat>(
+        2,    // axis
+        2);    // input_cnt
+
+    auto convolution_232 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_21 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_22 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_25 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_26 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_29 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_30 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_33 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_34 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_35 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_38 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_39 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        64,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_42 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_43 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_46 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_47 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_48 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_51 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_54 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_55 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_58 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_59 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_62 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_63 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_64 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_67 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_68 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_71 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_72 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_75 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_76 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_77 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_80 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_81 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_84 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_85 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_88 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_89 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_90 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_93 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_94 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        128,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_97 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_98 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_101 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_102 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_103 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_106 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_109 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_110 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_113 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_114 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_117 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_118 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_119 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_122 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_123 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_126 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_127 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_130 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_131 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_132 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_135 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_136 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_139 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_140 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_143 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_144 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_145 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_148 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_149 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_152 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_153 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_156 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_157 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_158 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_161 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_162 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_165 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_166 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_169 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_170 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_171 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_174 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_175 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        256,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_178 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_179 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        1024,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_182 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_183 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_184 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        2048,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto convolution_187 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({2,2}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_190 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_191 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_194 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_195 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        2048,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_198 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_199 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_200 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_203 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_204 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_207 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_208 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        2048,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_211 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_212 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_213 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto relu_216 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_217 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        512,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({3,3}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({1,1,1,1}),    // pad
+        0);    // multiplier
+
+    auto relu_220 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto convolution_221 = graph->CreateOperation <tim::vx::ops::Conv2d>(
+        2048,    // weights
+        tim::vx::PadType::NONE,    // padding
+        std::array<uint32_t, 2>({1,1}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        std::array<uint32_t, 2>({1,1}),    // dilation
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        0);    // multiplier
+
+    auto add_224 = graph->CreateOperation <tim::vx::ops::Add>();
+
+    auto relu_225 = graph->CreateOperation <tim::vx::ops::Relu>();
+
+    auto pooling_226 = graph->CreateOperation <tim::vx::ops::Pool2d>(
+        tim::vx::PoolType::AVG,    // type
+        std::array<uint32_t, 4>({0,0,0,0}),    // pad
+        std::array<uint32_t, 2>({7,7}),    // ksize
+        std::array<uint32_t, 2>({1,1}),    // stride
+        tim::vx::RoundType::CEILING);    // round_type
+
+    auto fullconnect_227 = graph->CreateOperation <tim::vx::ops::FullyConnected>(
+        2,    // axis
+        1000);    // weights
+
+    auto softmax_228 = graph->CreateOperation <tim::vx::ops::Softmax>(
+        1.0,    // beta
+        0);    // axis
+
+    (*convolution_1)
+        .BindInputs({input_0, convolution_1_weight, convolution_1_bias})
+        .BindOutputs({convolution_1_out0});
+
+    (*relu_4)
+        .BindInputs({convolution_1_out0})
+        .BindOutputs({relu_4_out0});
+
+    (*pooling_5)
+        .BindInputs({relu_4_out0})
+        .BindOutputs({pooling_5_out0});
+
+    (*convolution_9)
+        .BindInputs({pooling_5_out0, convolution_9_weight, convolution_9_bias})
+        .BindOutputs({convolution_9_out0});
+
+    (*relu_12)
+        .BindInputs({convolution_9_out0})
+        .BindOutputs({relu_12_out0});
+
+    (*convolution_13)
+        .BindInputs({relu_12_out0, convolution_13_weight, convolution_13_bias})
+        .BindOutputs({convolution_13_out0});
+
+    (*relu_16)
+        .BindInputs({convolution_13_out0})
+        .BindOutputs({relu_16_out0});
+
+    (*concat_231)
+        .BindInputs({pooling_5_out0,relu_16_out0})
+        .BindOutputs({concat_231_out0});
+
+    (*convolution_232)
+        .BindInputs({concat_231_out0, convolution_232_weight, convolution_232_bias})
+        .BindOutputs({convolution_232_out0});
+
+    (*relu_21)
+        .BindInputs({convolution_232_out0})
+        .BindOutputs({relu_21_out0});
+
+    (*convolution_22)
+        .BindInputs({relu_21_out0, convolution_22_weight, convolution_22_bias})
+        .BindOutputs({convolution_22_out0});
+
+    (*relu_25)
+        .BindInputs({convolution_22_out0})
+        .BindOutputs({relu_25_out0});
+
+    (*convolution_26)
+        .BindInputs({relu_25_out0, convolution_26_weight, convolution_26_bias})
+        .BindOutputs({convolution_26_out0});
+
+    (*relu_29)
+        .BindInputs({convolution_26_out0})
+        .BindOutputs({relu_29_out0});
+
+    (*convolution_30)
+        .BindInputs({relu_29_out0, convolution_30_weight, convolution_30_bias})
+        .BindOutputs({convolution_30_out0});
+
+    (*add_33)
+        .BindInputs({relu_21_out0,convolution_30_out0})
+        .BindOutputs({add_33_out0});
+
+    (*relu_34)
+        .BindInputs({add_33_out0})
+        .BindOutputs({relu_34_out0});
+
+    (*convolution_35)
+        .BindInputs({relu_34_out0, convolution_35_weight, convolution_35_bias})
+        .BindOutputs({convolution_35_out0});
+
+    (*relu_38)
+        .BindInputs({convolution_35_out0})
+        .BindOutputs({relu_38_out0});
+
+    (*convolution_39)
+        .BindInputs({relu_38_out0, convolution_39_weight, convolution_39_bias})
+        .BindOutputs({convolution_39_out0});
+
+    (*relu_42)
+        .BindInputs({convolution_39_out0})
+        .BindOutputs({relu_42_out0});
+
+    (*convolution_43)
+        .BindInputs({relu_42_out0, convolution_43_weight, convolution_43_bias})
+        .BindOutputs({convolution_43_out0});
+
+    (*add_46)
+        .BindInputs({relu_34_out0,convolution_43_out0})
+        .BindOutputs({add_46_out0});
+
+    (*relu_47)
+        .BindInputs({add_46_out0})
+        .BindOutputs({relu_47_out0});
+
+    (*convolution_48)
+        .BindInputs({relu_47_out0, convolution_48_weight, convolution_48_bias})
+        .BindOutputs({convolution_48_out0});
+
+    (*convolution_51)
+        .BindInputs({relu_47_out0, convolution_51_weight, convolution_51_bias})
+        .BindOutputs({convolution_51_out0});
+
+    (*relu_54)
+        .BindInputs({convolution_51_out0})
+        .BindOutputs({relu_54_out0});
+
+    (*convolution_55)
+        .BindInputs({relu_54_out0, convolution_55_weight, convolution_55_bias})
+        .BindOutputs({convolution_55_out0});
+
+    (*relu_58)
+        .BindInputs({convolution_55_out0})
+        .BindOutputs({relu_58_out0});
+
+    (*convolution_59)
+        .BindInputs({relu_58_out0, convolution_59_weight, convolution_59_bias})
+        .BindOutputs({convolution_59_out0});
+
+    (*add_62)
+        .BindInputs({convolution_48_out0,convolution_59_out0})
+        .BindOutputs({add_62_out0});
+
+    (*relu_63)
+        .BindInputs({add_62_out0})
+        .BindOutputs({relu_63_out0});
+
+    (*convolution_64)
+        .BindInputs({relu_63_out0, convolution_64_weight, convolution_64_bias})
+        .BindOutputs({convolution_64_out0});
+
+    (*relu_67)
+        .BindInputs({convolution_64_out0})
+        .BindOutputs({relu_67_out0});
+
+    (*convolution_68)
+        .BindInputs({relu_67_out0, convolution_68_weight, convolution_68_bias})
+        .BindOutputs({convolution_68_out0});
+
+    (*relu_71)
+        .BindInputs({convolution_68_out0})
+        .BindOutputs({relu_71_out0});
+
+    (*convolution_72)
+        .BindInputs({relu_71_out0, convolution_72_weight, convolution_72_bias})
+        .BindOutputs({convolution_72_out0});
+
+    (*add_75)
+        .BindInputs({relu_63_out0,convolution_72_out0})
+        .BindOutputs({add_75_out0});
+
+    (*relu_76)
+        .BindInputs({add_75_out0})
+        .BindOutputs({relu_76_out0});
+
+    (*convolution_77)
+        .BindInputs({relu_76_out0, convolution_77_weight, convolution_77_bias})
+        .BindOutputs({convolution_77_out0});
+
+    (*relu_80)
+        .BindInputs({convolution_77_out0})
+        .BindOutputs({relu_80_out0});
+
+    (*convolution_81)
+        .BindInputs({relu_80_out0, convolution_81_weight, convolution_81_bias})
+        .BindOutputs({convolution_81_out0});
+
+    (*relu_84)
+        .BindInputs({convolution_81_out0})
+        .BindOutputs({relu_84_out0});
+
+    (*convolution_85)
+        .BindInputs({relu_84_out0, convolution_85_weight, convolution_85_bias})
+        .BindOutputs({convolution_85_out0});
+
+    (*add_88)
+        .BindInputs({relu_76_out0,convolution_85_out0})
+        .BindOutputs({add_88_out0});
+
+    (*relu_89)
+        .BindInputs({add_88_out0})
+        .BindOutputs({relu_89_out0});
+
+    (*convolution_90)
+        .BindInputs({relu_89_out0, convolution_90_weight, convolution_90_bias})
+        .BindOutputs({convolution_90_out0});
+
+    (*relu_93)
+        .BindInputs({convolution_90_out0})
+        .BindOutputs({relu_93_out0});
+
+    (*convolution_94)
+        .BindInputs({relu_93_out0, convolution_94_weight, convolution_94_bias})
+        .BindOutputs({convolution_94_out0});
+
+    (*relu_97)
+        .BindInputs({convolution_94_out0})
+        .BindOutputs({relu_97_out0});
+
+    (*convolution_98)
+        .BindInputs({relu_97_out0, convolution_98_weight, convolution_98_bias})
+        .BindOutputs({convolution_98_out0});
+
+    (*add_101)
+        .BindInputs({relu_89_out0,convolution_98_out0})
+        .BindOutputs({add_101_out0});
+
+    (*relu_102)
+        .BindInputs({add_101_out0})
+        .BindOutputs({relu_102_out0});
+
+    (*convolution_103)
+        .BindInputs({relu_102_out0, convolution_103_weight, convolution_103_bias})
+        .BindOutputs({convolution_103_out0});
+
+    (*convolution_106)
+        .BindInputs({relu_102_out0, convolution_106_weight, convolution_106_bias})
+        .BindOutputs({convolution_106_out0});
+
+    (*relu_109)
+        .BindInputs({convolution_106_out0})
+        .BindOutputs({relu_109_out0});
+
+    (*convolution_110)
+        .BindInputs({relu_109_out0, convolution_110_weight, convolution_110_bias})
+        .BindOutputs({convolution_110_out0});
+
+    (*relu_113)
+        .BindInputs({convolution_110_out0})
+        .BindOutputs({relu_113_out0});
+
+    (*convolution_114)
+        .BindInputs({relu_113_out0, convolution_114_weight, convolution_114_bias})
+        .BindOutputs({convolution_114_out0});
+
+    (*add_117)
+        .BindInputs({convolution_103_out0,convolution_114_out0})
+        .BindOutputs({add_117_out0});
+
+    (*relu_118)
+        .BindInputs({add_117_out0})
+        .BindOutputs({relu_118_out0});
+
+    (*convolution_119)
+        .BindInputs({relu_118_out0, convolution_119_weight, convolution_119_bias})
+        .BindOutputs({convolution_119_out0});
+
+    (*relu_122)
+        .BindInputs({convolution_119_out0})
+        .BindOutputs({relu_122_out0});
+
+    (*convolution_123)
+        .BindInputs({relu_122_out0, convolution_123_weight, convolution_123_bias})
+        .BindOutputs({convolution_123_out0});
+
+    (*relu_126)
+        .BindInputs({convolution_123_out0})
+        .BindOutputs({relu_126_out0});
+
+    (*convolution_127)
+        .BindInputs({relu_126_out0, convolution_127_weight, convolution_127_bias})
+        .BindOutputs({convolution_127_out0});
+
+    (*add_130)
+        .BindInputs({relu_118_out0,convolution_127_out0})
+        .BindOutputs({add_130_out0});
+
+    (*relu_131)
+        .BindInputs({add_130_out0})
+        .BindOutputs({relu_131_out0});
+
+    (*convolution_132)
+        .BindInputs({relu_131_out0, convolution_132_weight, convolution_132_bias})
+        .BindOutputs({convolution_132_out0});
+
+    (*relu_135)
+        .BindInputs({convolution_132_out0})
+        .BindOutputs({relu_135_out0});
+
+    (*convolution_136)
+        .BindInputs({relu_135_out0, convolution_136_weight, convolution_136_bias})
+        .BindOutputs({convolution_136_out0});
+
+    (*relu_139)
+        .BindInputs({convolution_136_out0})
+        .BindOutputs({relu_139_out0});
+
+    (*convolution_140)
+        .BindInputs({relu_139_out0, convolution_140_weight, convolution_140_bias})
+        .BindOutputs({convolution_140_out0});
+
+    (*add_143)
+        .BindInputs({relu_131_out0,convolution_140_out0})
+        .BindOutputs({add_143_out0});
+
+    (*relu_144)
+        .BindInputs({add_143_out0})
+        .BindOutputs({relu_144_out0});
+
+    (*convolution_145)
+        .BindInputs({relu_144_out0, convolution_145_weight, convolution_145_bias})
+        .BindOutputs({convolution_145_out0});
+
+    (*relu_148)
+        .BindInputs({convolution_145_out0})
+        .BindOutputs({relu_148_out0});
+
+    (*convolution_149)
+        .BindInputs({relu_148_out0, convolution_149_weight, convolution_149_bias})
+        .BindOutputs({convolution_149_out0});
+
+    (*relu_152)
+        .BindInputs({convolution_149_out0})
+        .BindOutputs({relu_152_out0});
+
+    (*convolution_153)
+        .BindInputs({relu_152_out0, convolution_153_weight, convolution_153_bias})
+        .BindOutputs({convolution_153_out0});
+
+    (*add_156)
+        .BindInputs({relu_144_out0,convolution_153_out0})
+        .BindOutputs({add_156_out0});
+
+    (*relu_157)
+        .BindInputs({add_156_out0})
+        .BindOutputs({relu_157_out0});
+
+    (*convolution_158)
+        .BindInputs({relu_157_out0, convolution_158_weight, convolution_158_bias})
+        .BindOutputs({convolution_158_out0});
+
+    (*relu_161)
+        .BindInputs({convolution_158_out0})
+        .BindOutputs({relu_161_out0});
+
+    (*convolution_162)
+        .BindInputs({relu_161_out0, convolution_162_weight, convolution_162_bias})
+        .BindOutputs({convolution_162_out0});
+
+    (*relu_165)
+        .BindInputs({convolution_162_out0})
+        .BindOutputs({relu_165_out0});
+
+    (*convolution_166)
+        .BindInputs({relu_165_out0, convolution_166_weight, convolution_166_bias})
+        .BindOutputs({convolution_166_out0});
+
+    (*add_169)
+        .BindInputs({relu_157_out0,convolution_166_out0})
+        .BindOutputs({add_169_out0});
+
+    (*relu_170)
+        .BindInputs({add_169_out0})
+        .BindOutputs({relu_170_out0});
+
+    (*convolution_171)
+        .BindInputs({relu_170_out0, convolution_171_weight, convolution_171_bias})
+        .BindOutputs({convolution_171_out0});
+
+    (*relu_174)
+        .BindInputs({convolution_171_out0})
+        .BindOutputs({relu_174_out0});
+
+    (*convolution_175)
+        .BindInputs({relu_174_out0, convolution_175_weight, convolution_175_bias})
+        .BindOutputs({convolution_175_out0});
+
+    (*relu_178)
+        .BindInputs({convolution_175_out0})
+        .BindOutputs({relu_178_out0});
+
+    (*convolution_179)
+        .BindInputs({relu_178_out0, convolution_179_weight, convolution_179_bias})
+        .BindOutputs({convolution_179_out0});
+
+    (*add_182)
+        .BindInputs({relu_170_out0,convolution_179_out0})
+        .BindOutputs({add_182_out0});
+
+    (*relu_183)
+        .BindInputs({add_182_out0})
+        .BindOutputs({relu_183_out0});
+
+    (*convolution_184)
+        .BindInputs({relu_183_out0, convolution_184_weight, convolution_184_bias})
+        .BindOutputs({convolution_184_out0});
+
+    (*convolution_187)
+        .BindInputs({relu_183_out0, convolution_187_weight, convolution_187_bias})
+        .BindOutputs({convolution_187_out0});
+
+    (*relu_190)
+        .BindInputs({convolution_187_out0})
+        .BindOutputs({relu_190_out0});
+
+    (*convolution_191)
+        .BindInputs({relu_190_out0, convolution_191_weight, convolution_191_bias})
+        .BindOutputs({convolution_191_out0});
+
+    (*relu_194)
+        .BindInputs({convolution_191_out0})
+        .BindOutputs({relu_194_out0});
+
+    (*convolution_195)
+        .BindInputs({relu_194_out0, convolution_195_weight, convolution_195_bias})
+        .BindOutputs({convolution_195_out0});
+
+    (*add_198)
+        .BindInputs({convolution_184_out0,convolution_195_out0})
+        .BindOutputs({add_198_out0});
+
+    (*relu_199)
+        .BindInputs({add_198_out0})
+        .BindOutputs({relu_199_out0});
+
+    (*convolution_200)
+        .BindInputs({relu_199_out0, convolution_200_weight, convolution_200_bias})
+        .BindOutputs({convolution_200_out0});
+
+    (*relu_203)
+        .BindInputs({convolution_200_out0})
+        .BindOutputs({relu_203_out0});
+
+    (*convolution_204)
+        .BindInputs({relu_203_out0, convolution_204_weight, convolution_204_bias})
+        .BindOutputs({convolution_204_out0});
+
+    (*relu_207)
+        .BindInputs({convolution_204_out0})
+        .BindOutputs({relu_207_out0});
+
+    (*convolution_208)
+        .BindInputs({relu_207_out0, convolution_208_weight, convolution_208_bias})
+        .BindOutputs({convolution_208_out0});
+
+    (*add_211)
+        .BindInputs({relu_199_out0,convolution_208_out0})
+        .BindOutputs({add_211_out0});
+
+    (*relu_212)
+        .BindInputs({add_211_out0})
+        .BindOutputs({relu_212_out0});
+
+    (*convolution_213)
+        .BindInputs({relu_212_out0, convolution_213_weight, convolution_213_bias})
+        .BindOutputs({convolution_213_out0});
+
+    (*relu_216)
+        .BindInputs({convolution_213_out0})
+        .BindOutputs({relu_216_out0});
+
+    (*convolution_217)
+        .BindInputs({relu_216_out0, convolution_217_weight, convolution_217_bias})
+        .BindOutputs({convolution_217_out0});
+
+    (*relu_220)
+        .BindInputs({convolution_217_out0})
+        .BindOutputs({relu_220_out0});
+
+    (*convolution_221)
+        .BindInputs({relu_220_out0, convolution_221_weight, convolution_221_bias})
+        .BindOutputs({convolution_221_out0});
+
+    (*add_224)
+        .BindInputs({relu_212_out0,convolution_221_out0})
+        .BindOutputs({add_224_out0});
+
+    (*relu_225)
+        .BindInputs({add_224_out0})
+        .BindOutputs({relu_225_out0});
+
+    (*pooling_226)
+        .BindInputs({relu_225_out0})
+        .BindOutputs({pooling_226_out0});
+
+    (*fullconnect_227)
+        .BindInputs({pooling_226_out0, fullconnect_227_weight, fullconnect_227_bias})
+        .BindOutputs({fullconnect_227_out0});
+
+    (*softmax_228)
+        .BindInputs({fullconnect_227_out0})
+        .BindOutputs({output_229});
+
+    free(coef_data_ptr);
+}
+
+}  // namespace acuitylite
diff --git a/samples/multi_device/vx_resnet50.h b/samples/multi_device/vx_resnet50.h
new file mode 100644
index 0000000..dc9aeac
--- /dev/null
+++ b/samples/multi_device/vx_resnet50.h
@@ -0,0 +1,34 @@
+/****************************************************************************
+*   Generated by ACUITY 6.6.0
+*   Match timvx 1.1.30
+*
+*   Neural Network appliction network definition header file
+****************************************************************************/
+#ifndef _VX_RESNET50_H
+#define _VX_RESNET50_H
+
+#include "tim/vx/operation.h"
+#include "tim/vx/tensor.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/ops.h"
+
+namespace acuitylite
+{
+
+class resnet50
+{
+    public:
+        using input_0_type = uint8_t;
+        using output_0_type = uint16_t;
+        static std::vector<std::vector<uint32_t>> input_size_list;
+        static std::vector<uint32_t> input_bytes_list;
+        static std::vector<std::vector<uint32_t>> output_size_list;
+        static std::vector<std::shared_ptr<tim::vx::Tensor>> inputs_tensor;
+        static std::vector<std::shared_ptr<tim::vx::Tensor>> outputs_tensor;
+
+        static void construct_graph(std::shared_ptr<tim::vx::Graph> graph, const char *data_file_name);
+};
+
+}  // namespace acuitylite
+
+#endif