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for given IR
module { func.func @torch_jit(%arg0: !torch.vtensor<[1,3,224,224],f32>, %arg2: !torch.vtensor<[?,64,?,?],f32> , %arg3: !torch.vtensor<[?,?,?,?,?,?],f32> , %arg4: !torch.vtensor<[1],si64> ) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 21 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.12.1"} { %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64x64x3x3xf32>} : () -> !torch.vtensor<[64,64,3,3],f32> %1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %2 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %3 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %4 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %5 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %6 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<16x256x1x1xf32>} : () -> !torch.vtensor<[16,256,1,1],f32> %7 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<16xf32>} : () -> !torch.vtensor<[16],f32> %8 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<256x16x1x1xf32>} : () -> !torch.vtensor<[256,16,1,1],f32> %9 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> %10 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64x256x1x1xf32>} : () -> !torch.vtensor<[64,256,1,1],f32> %11 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %12 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %13 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %14 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<192xf32>} : () -> !torch.vtensor<[192],f32> %322 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64x3x3x3xf32>} : () -> !torch.vtensor<[64,3,3,3],f32> %323 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> %324 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<256x64x1x1xf32>} : () -> !torch.vtensor<[256,64,1,1],f32> %325 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> %326 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<256x1x3x3xf32>} : () -> !torch.vtensor<[256,1,3,3],f32> %327 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> %368 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %369 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<4xsi64>} : () -> !torch.vtensor<[4],si64> %370 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<si64>} : () -> !torch.vtensor<[],si64> %371 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %372 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %373 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0.0> : tensor<64x192xf32>} : () -> !torch.vtensor<[64,192],f32> %374 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_onnx__Concat_6281> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %375 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_onnx__Concat_6282> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %376 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_onnx__Concat_6283> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %788 = torch.operator "onnx.Identity"(%372) : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %789 = torch.operator "onnx.Identity"(%371) : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %790 = torch.operator "onnx.Identity"(%371) : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %791 = torch.operator "onnx.Identity"(%371) : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %792 = torch.operator "onnx.ConstantOfShape"(%368) {torch.onnx.value = dense_resource<_> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64> %793 = torch.operator "onnx.Concat"(%369, %792) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[8],si64> %794 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> %795 = torch.operator "onnx.Reshape"(%793, %794) : (!torch.vtensor<[8],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4,2],si64> %796 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %797 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %798 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__4> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %799 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__5> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %800 = torch.operator "onnx.Slice"(%795, %797, %798, %796, %799) : (!torch.vtensor<[4,2],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,2],si64> %801 = torch.operator "onnx.Transpose"(%800) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[4,2],si64>) -> !torch.vtensor<[2,4],si64> %802 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__6> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %803 = torch.operator "onnx.Reshape"(%801, %802) : (!torch.vtensor<[2,4],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[8],si64> %804 = torch.operator "onnx.Cast"(%803) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[8],si64>) -> !torch.vtensor<[8],si64> %805 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__7> : tensor<f32>} : () -> !torch.vtensor<[],f32> %806 = torch.operator "onnx.Pad"(%arg0, %804, %805) {torch.onnx.mode = "constant"} : (!torch.vtensor<[1,3,224,224],f32>, !torch.vtensor<[8],si64>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?,?,?],f32> %807 = torch.operator "onnx.Conv"(%806, %322, %323) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[64,3,3,3],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,64,?,?],f32> %808 = torch.operator "onnx.Mul"(%807, %807) : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %809 = torch.operator "onnx.Mul"(%807, %808) : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %810 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__8> : tensor<f32>} : () -> !torch.vtensor<[],f32> %811 = torch.operator "onnx.Mul"(%810, %809) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %812 = torch.operator "onnx.Add"(%807, %811) : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %813 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__9> : tensor<f32>} : () -> !torch.vtensor<[],f32> %814 = torch.operator "onnx.Mul"(%813, %812) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %815 = torch.operator "onnx.Tanh"(%814) : (!torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %816 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__10> : tensor<f32>} : () -> !torch.vtensor<[],f32> %817 = torch.operator "onnx.Add"(%816, %815) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %818 = torch.operator "onnx.Mul"(%807, %817) : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %819 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__11> : tensor<f32>} : () -> !torch.vtensor<[],f32> %820 = torch.operator "onnx.Mul"(%819, %818) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %821 = torch.operator "onnx.Conv"(%820, %0, %1) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [1 : si64, 1 : si64, 1 : si64, 1 : si64], torch.onnx.strides = [1 : si64, 1 : si64]} : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,64,?,?],f32> %822 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__12> : tensor<8xsi64>} : () -> !torch.vtensor<[8],si64> %823 = torch.operator "onnx.Pad"(%821, %822) {torch.onnx.mode = "constant"} : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[8],si64>) -> !torch.vtensor<[?,64,?,?],f32> %824 = torch.operator "onnx.AveragePool"(%823) {torch.onnx.ceil_mode = 0 : si64, torch.onnx.kernel_shape = [2 : si64, 2 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %825 = torch.operator "onnx.BatchNormalization"(%821, %2, %3, %4, %5) {torch.onnx.epsilon = 1.000000e-03 : f32, torch.onnx.momentum = 0.899999976 : f32} : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[64],f32>, !torch.vtensor<[64],f32>, !torch.vtensor<[64],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,64,?,?],f32> %826 = torch.operator "onnx.Conv"(%825, %324, %325) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [1 : si64, 1 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [1 : si64, 1 : si64]} : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[?,256,?,?],f32> %827 = torch.operator "onnx.Mul"(%826, %826) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %828 = torch.operator "onnx.Mul"(%826, %827) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %829 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__13> : tensor<f32>} : () -> !torch.vtensor<[],f32> %830 = torch.operator "onnx.Mul"(%829, %828) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %831 = torch.operator "onnx.Add"(%826, %830) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %832 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__14> : tensor<f32>} : () -> !torch.vtensor<[],f32> %833 = torch.operator "onnx.Mul"(%832, %831) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %834 = torch.operator "onnx.Tanh"(%833) : (!torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %835 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__15> : tensor<f32>} : () -> !torch.vtensor<[],f32> %836 = torch.operator "onnx.Add"(%835, %834) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %837 = torch.operator "onnx.Mul"(%826, %836) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %838 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__16> : tensor<f32>} : () -> !torch.vtensor<[],f32> %839 = torch.operator "onnx.Mul"(%838, %837) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %840 = torch.operator "onnx.Shape"(%839) : (!torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[4],si64> %841 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__17> : tensor<si64>} : () -> !torch.vtensor<[],si64> %842 = torch.operator "onnx.Gather"(%840, %841) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %843 = torch.operator "onnx.Shape"(%839) : (!torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[4],si64> %844 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__18> : tensor<si64>} : () -> !torch.vtensor<[],si64> %845 = torch.operator "onnx.Gather"(%843, %844) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %846 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__19> : tensor<si64>} : () -> !torch.vtensor<[],si64> %847 = torch.operator "onnx.Sub"(%846, %842) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %848 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__20> : tensor<si64>} : () -> !torch.vtensor<[],si64> %849 = torch.operator "onnx.Sub"(%848, %845) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %850 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__21> : tensor<si64>} : () -> !torch.vtensor<[],si64> %851 = torch.operator "onnx.Div"(%849, %850) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %852 = torch.operator "onnx.Cast"(%851) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %853 = torch.operator "onnx.Cast"(%852) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %854 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__22> : tensor<si64>} : () -> !torch.vtensor<[],si64> %855 = torch.operator "onnx.Div"(%849, %854) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %856 = torch.operator "onnx.Cast"(%855) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %857 = torch.operator "onnx.Cast"(%856) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %858 = torch.operator "onnx.Sub"(%849, %857) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %859 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__23> : tensor<si64>} : () -> !torch.vtensor<[],si64> %860 = torch.operator "onnx.Div"(%847, %859) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %861 = torch.operator "onnx.Cast"(%860) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %862 = torch.operator "onnx.Cast"(%861) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %863 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__24> : tensor<si64>} : () -> !torch.vtensor<[],si64> %864 = torch.operator "onnx.Div"(%847, %863) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %865 = torch.operator "onnx.Cast"(%864) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %866 = torch.operator "onnx.Cast"(%865) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %867 = torch.operator "onnx.Sub"(%847, %866) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %868 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__25> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %869 = torch.operator "onnx.Unsqueeze"(%853, %868) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %870 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__26> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %871 = torch.operator "onnx.Unsqueeze"(%858, %870) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %872 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__27> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %873 = torch.operator "onnx.Unsqueeze"(%862, %872) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %874 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__28> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %875 = torch.operator "onnx.Unsqueeze"(%867, %874) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %876 = torch.operator "onnx.Concat"(%869, %871, %873, %875) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64> %877 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__29> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %878 = torch.operator "onnx.Unsqueeze"(%853, %877) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %879 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__30> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %880 = torch.operator "onnx.Unsqueeze"(%858, %879) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %881 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__31> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %882 = torch.operator "onnx.Unsqueeze"(%862, %881) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %883 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__32> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %884 = torch.operator "onnx.Unsqueeze"(%867, %883) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %885 = torch.operator "onnx.Concat"(%878, %880, %882, %884) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64> %886 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__33> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %888 = torch.operator "onnx.Gather"(%arg4, %886) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %889 = torch.operator "onnx.Sub"(%370, %888) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %890 = torch.operator "onnx.Cast"(%885) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[4],si64>) -> !torch.vtensor<[4],si64> %891 = torch.operator "onnx.ConstantOfShape"(%889) {torch.onnx.value = dense_resource<__34> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64> %892 = torch.operator "onnx.Concat"(%890, %891) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[8],si64> %893 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__35> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> %894 = torch.operator "onnx.Reshape"(%892, %893) : (!torch.vtensor<[8],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4,2],si64> %895 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__36> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %896 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__37> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %897 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__38> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %898 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__39> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %899 = torch.operator "onnx.Slice"(%894, %896, %897, %895, %898) : (!torch.vtensor<[4,2],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,2],si64> %900 = torch.operator "onnx.Transpose"(%899) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[4,2],si64>) -> !torch.vtensor<[2,4],si64> %901 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__40> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %902 = torch.operator "onnx.Reshape"(%900, %901) : (!torch.vtensor<[2,4],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[8],si64> %903 = torch.operator "onnx.Cast"(%902) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[8],si64>) -> !torch.vtensor<[8],si64> %904 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__41> : tensor<f32>} : () -> !torch.vtensor<[],f32> %905 = torch.operator "onnx.Pad"(%839, %903, %904) {torch.onnx.mode = "constant"} : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[8],si64>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?,?,?],f32> %906 = torch.operator "onnx.Conv"(%905, %326, %327) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 256 : si64, torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[256,1,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[?,256,?,?],f32> %907 = torch.operator "onnx.Mul"(%906, %906) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %908 = torch.operator "onnx.Mul"(%906, %907) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %909 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__42> : tensor<f32>} : () -> !torch.vtensor<[],f32> %910 = torch.operator "onnx.Mul"(%909, %908) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %911 = torch.operator "onnx.Add"(%906, %910) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %912 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__43> : tensor<f32>} : () -> !torch.vtensor<[],f32> %913 = torch.operator "onnx.Mul"(%912, %911) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %914 = torch.operator "onnx.Tanh"(%913) : (!torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %915 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__44> : tensor<f32>} : () -> !torch.vtensor<[],f32> %916 = torch.operator "onnx.Add"(%915, %914) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %917 = torch.operator "onnx.Mul"(%906, %916) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %918 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__45> : tensor<f32>} : () -> !torch.vtensor<[],f32> %919 = torch.operator "onnx.Mul"(%918, %917) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,256,?,?],f32>) -> !torch.vtensor<[?,256,?,?],f32> %920 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<[2, 3]> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> %921 = torch.operator "onnx.ReduceMean"(%919, %920) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,256,1,1],f32> %922 = torch.operator "onnx.Conv"(%921, %6, %7) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [1 : si64, 1 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [1 : si64, 1 : si64]} : (!torch.vtensor<[?,256,1,1],f32>, !torch.vtensor<[16,256,1,1],f32>, !torch.vtensor<[16],f32>) -> !torch.vtensor<[?,16,1,1],f32> %923 = torch.operator "onnx.Sigmoid"(%922) : (!torch.vtensor<[?,16,1,1],f32>) -> !torch.vtensor<[?,16,1,1],f32> %924 = torch.operator "onnx.Mul"(%922, %923) : (!torch.vtensor<[?,16,1,1],f32>, !torch.vtensor<[?,16,1,1],f32>) -> !torch.vtensor<[?,16,1,1],f32> %925 = torch.operator "onnx.Conv"(%924, %8, %9) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [1 : si64, 1 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [1 : si64, 1 : si64]} : (!torch.vtensor<[?,16,1,1],f32>, !torch.vtensor<[256,16,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[?,256,1,1],f32> %926 = torch.operator "onnx.Sigmoid"(%925) : (!torch.vtensor<[?,256,1,1],f32>) -> !torch.vtensor<[?,256,1,1],f32> %927 = torch.operator "onnx.Mul"(%919, %926) : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[?,256,1,1],f32>) -> !torch.vtensor<[?,256,?,?],f32> %928 = torch.operator "onnx.Conv"(%927, %10, %11) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [1 : si64, 1 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [1 : si64, 1 : si64]} : (!torch.vtensor<[?,256,?,?],f32>, !torch.vtensor<[64,256,1,1],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,64,?,?],f32> %929 = torch.operator "onnx.Add"(%928, %824) : (!torch.vtensor<[?,64,?,?],f32>, !torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,64,?,?],f32> %930 = torch.operator "onnx.Transpose"(%929) {torch.onnx.perm = [0 : si64, 2 : si64, 3 : si64, 1 : si64]} : (!torch.vtensor<[?,64,?,?],f32>) -> !torch.vtensor<[?,?,?,64],f32> %931 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<-1> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %932 = torch.operator "onnx.ReduceMean"(%930, %931) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?,?,1],f32> %933 = torch.operator "onnx.Sub"(%930, %932) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[?,?,?,1],f32>) -> !torch.vtensor<[?,?,?,64],f32> %934 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__46> : tensor<f32>} : () -> !torch.vtensor<[],f32> %935 = torch.operator "onnx.Pow"(%933, %934) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?,?,64],f32> %936 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<-1> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %937 = torch.operator "onnx.ReduceMean"(%935, %936) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?,?,1],f32> %938 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__47> : tensor<f32>} : () -> !torch.vtensor<[],f32> %939 = torch.operator "onnx.Add"(%937, %938) : (!torch.vtensor<[?,?,?,1],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?,?,1],f32> %940 = torch.operator "onnx.Sqrt"(%939) : (!torch.vtensor<[?,?,?,1],f32>) -> !torch.vtensor<[?,?,?,1],f32> %941 = torch.operator "onnx.Div"(%933, %940) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[?,?,?,1],f32>) -> !torch.vtensor<[?,?,?,64],f32> %942 = torch.operator "onnx.Mul"(%941, %12) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?,?,64],f32> %943 = torch.operator "onnx.Add"(%942, %13) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[?,?,?,64],f32> %944 = torch.operator "onnx.Shape"(%943) : (!torch.vtensor<[?,?,?,64],f32>) -> !torch.vtensor<[4],si64> %945 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__48> : tensor<si64>} : () -> !torch.vtensor<[],si64> %946 = torch.operator "onnx.Gather"(%944, %945) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %947 = torch.operator "onnx.Shape"(%943) : (!torch.vtensor<[?,?,?,64],f32>) -> !torch.vtensor<[4],si64> %948 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__49> : tensor<si64>} : () -> !torch.vtensor<[],si64> %949 = torch.operator "onnx.Gather"(%947, %948) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %950 = torch.operator "onnx.Shape"(%943) : (!torch.vtensor<[?,?,?,64],f32>) -> !torch.vtensor<[4],si64> %951 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__50> : tensor<si64>} : () -> !torch.vtensor<[],si64> %952 = torch.operator "onnx.Gather"(%950, %951) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %953 = torch.operator "onnx.Shape"(%943) : (!torch.vtensor<[?,?,?,64],f32>) -> !torch.vtensor<[4],si64> %954 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__51> : tensor<si64>} : () -> !torch.vtensor<[],si64> %955 = torch.operator "onnx.Gather"(%953, %954) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %956 = torch.operator "onnx.Shape"(%943) : (!torch.vtensor<[?,?,?,64],f32>) -> !torch.vtensor<[4],si64> %957 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__52> : tensor<si64>} : () -> !torch.vtensor<[],si64> %958 = torch.operator "onnx.Gather"(%956, %957) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %959 = torch.operator "onnx.Shape"(%943) : (!torch.vtensor<[?,?,?,64],f32>) -> !torch.vtensor<[4],si64> %960 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__53> : tensor<si64>} : () -> !torch.vtensor<[],si64> %961 = torch.operator "onnx.Gather"(%959, %960) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %962 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__54> : tensor<si64>} : () -> !torch.vtensor<[],si64> %963 = torch.operator "onnx.Div"(%955, %962) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %964 = torch.operator "onnx.Cast"(%963) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %965 = torch.operator "onnx.Cast"(%964) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %966 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__55> : tensor<si64>} : () -> !torch.vtensor<[],si64> %967 = torch.operator "onnx.Div"(%958, %966) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %968 = torch.operator "onnx.Cast"(%967) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %969 = torch.operator "onnx.Cast"(%968) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %970 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__56> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %971 = torch.operator "onnx.Unsqueeze"(%952, %970) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %972 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__57> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %973 = torch.operator "onnx.Unsqueeze"(%965, %972) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %974 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__58> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %975 = torch.operator "onnx.Unsqueeze"(%969, %974) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %976 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__59> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %977 = torch.operator "onnx.Unsqueeze"(%961, %976) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %978 = torch.operator "onnx.Concat"(%971, %973, %371, %975, %791, %977) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[6],si64> %979 = torch.operator "onnx.Reshape"(%943, %978) : (!torch.vtensor<[?,?,?,64],f32>, !torch.vtensor<[6],si64>) -> !torch.vtensor<[?,?,?,?,?,?],f32> %980 = torch.operator "onnx.Transpose"(%979) {torch.onnx.perm = [0 : si64, 1 : si64, 3 : si64, 2 : si64, 4 : si64, 5 : si64]} : (!torch.vtensor<[?,?,?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?,?,?],f32> %981 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__60> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %982 = torch.operator "onnx.Unsqueeze"(%961, %981) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %983 = torch.operator "onnx.Concat"(%372, %790, %789, %982) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],si64> %984 = torch.operator "onnx.Reshape"(%980, %983) : (!torch.vtensor<[?,?,?,?,?,?],f32>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> %985 = torch.operator "onnx.Shape"(%984) : (!torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[4],si64> %986 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__61> : tensor<si64>} : () -> !torch.vtensor<[],si64> %987 = torch.operator "onnx.Gather"(%985, %986) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> %997 = torch.operator "onnx.MatMul"(%984, %373) : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[64,192],f32>) -> !torch.vtensor<[?,?,?,192],f32> %998 = torch.operator "onnx.Add"(%14, %997) : (!torch.vtensor<[192],f32>, !torch.vtensor<[?,?,?,192],f32>) -> !torch.vtensor<[?,?,?,192],f32> %999 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__65> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %1000 = torch.operator "onnx.Unsqueeze"(%987, %999) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> %1001 = torch.operator "onnx.Concat"(%1000, %788, %374, %375, %376) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[5],si64> %1002 = torch.operator "onnx.Reshape"(%998, %1001) : (!torch.vtensor<[?,?,?,192],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> %1003 = torch.operator "onnx.Transpose"(%1002) {torch.onnx.perm = [0 : si64, 3 : si64, 2 : si64, 1 : si64, 4 : si64]} : (!torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?,?],f32> %1004 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__66> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64> %1005:3 = torch.operator "onnx.Split"(%1003, %1004) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[3],si64>) -> (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?,?,?,?,?],f32>) %1006 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__67> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> %1007 = torch.operator "onnx.Squeeze"(%1005#0, %1006) : (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?,?,?],f32> return %1007 : !torch.vtensor<[?,?,?,?],f32> } } {-# dialect_resources: { builtin: { _onnx__Concat_6281: "0x080000000300000000000000", _onnx__Concat_6282: "0x080000000200000000000000", _onnx__Concat_6283: "0x080000002000000000000000", _: "0x080000000000000000000000", __1: "0x08000000FFFFFFFFFFFFFFFF0200000000000000", __2: "0x080000000000000000000000", __3: "0x08000000FFFFFFFFFFFFFFFF", __4: "0x080000000100000000000080", __5: "0x08000000FFFFFFFFFFFFFFFF", __6: "0x08000000FFFFFFFFFFFFFFFF", __7: "0x0800000000000000", __8: "0x080000001327373D", __9: "0x080000002A424C3F", __10: "0x080000000000803F", __11: "0x080000000000003F", __12: "0x0800000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000", __13: "0x080000001327373D", __14: "0x080000002A424C3F", __15: "0x080000000000803F", __16: "0x080000000000003F", __17: "0x080000000200000000000000", __18: "0x080000000300000000000000", __19: "0x080000007100000000000000", __20: "0x080000007100000000000000", __21: "0x080000000200000000000000", __22: "0x080000000200000000000000", __23: "0x080000000200000000000000", __24: "0x080000000200000000000000", __25: "0x080000000000000000000000", __26: "0x080000000000000000000000", __27: "0x080000000000000000000000", __28: "0x080000000000000000000000", __29: "0x080000000000000000000000", __30: "0x080000000000000000000000", __31: "0x080000000000000000000000", __32: "0x080000000000000000000000", __33: "0x080000000000000000000000", __34: "0x080000000000000000000000", __35: "0x08000000FFFFFFFFFFFFFFFF0200000000000000", __36: "0x080000000000000000000000", __37: "0x08000000FFFFFFFFFFFFFFFF", __38: "0x080000000100000000000080", __39: "0x08000000FFFFFFFFFFFFFFFF", __40: "0x08000000FFFFFFFFFFFFFFFF", __41: "0x0800000000000000", __42: "0x080000001327373D", __43: "0x080000002A424C3F", __44: "0x080000000000803F", __45: "0x080000000000003F", __46: "0x0800000000000040", __47: "0x08000000ACC52737", __48: "0x080000000100000000000000", __49: "0x080000000200000000000000", __50: "0x080000000000000000000000", __51: "0x080000000100000000000000", __52: "0x080000000200000000000000", __53: "0x080000000300000000000000", __54: "0x080000000700000000000000", __55: "0x080000000700000000000000", __56: "0x080000000000000000000000", __57: "0x080000000000000000000000", __58: "0x080000000000000000000000", __59: "0x080000000000000000000000", __60: "0x080000000000000000000000", __61: "0x080000000000000000000000", __62: "0x080000000000000000000000", __63: "0x080000000100000000000000", __64: "0x080000000200000000000000", __65: "0x080000000000000000000000", __66: "0x08000000010000000000000001000000000000000100000000000000", __67: "0x080000000200000000000000" } } #-}
command : iree-compile --iree-hal-target-backends=llvm-cpu model.torch_onnx.mlir
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We really need to figure out how to untangle pads from torch exported models.
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zjgarvey
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for given IR
command : iree-compile --iree-hal-target-backends=llvm-cpu model.torch_onnx.mlir
The text was updated successfully, but these errors were encountered: