KeypointRCNN_vaiq_int8.default.torch-onnx.stripped.mlir

module {
  func.func @torch_jit(%arg0: !torch.vtensor<[3,300,400],f32>, %arg1: !torch.vtensor<[3,500,400],f32>) -> (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.vtensor<[?],si64>, !torch.vtensor<[?],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?],f32>, !torch.vtensor<[?],si64>, !torch.vtensor<[?],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "1.13.1"} {
    %none = torch.constant.none
    %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<_> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__4> : tensor<1x3x4xf32>} : () -> !torch.vtensor<[1,3,4],f32> 
    %5 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__5> : tensor<1x3x4xf32>} : () -> !torch.vtensor<[1,3,4],f32> 
    %6 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__6> : tensor<1x3x4xf32>} : () -> !torch.vtensor<[1,3,4],f32> 
    %7 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__7> : tensor<1x3x4xf32>} : () -> !torch.vtensor<[1,3,4],f32> 
    %8 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__8> : tensor<1x3x4xf32>} : () -> !torch.vtensor<[1,3,4],f32> 
    %9 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__9> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %10 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__10> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %11 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__11> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %12 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__12> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %13 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__13> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %14 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__14> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %15 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__15> : tensor<2048xf32>} : () -> !torch.vtensor<[2048],f32> 
    %16 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__16> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %17 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__17> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %18 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__18> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %19 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__19> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %20 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__20> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %21 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__21> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %22 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__22> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %23 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__23> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %24 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__24> : tensor<2048xf32>} : () -> !torch.vtensor<[2048],f32> 
    %25 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__25> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %26 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__26> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %27 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__27> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %28 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__28> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %29 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__29> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %30 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__30> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %31 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__31> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %32 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__32> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %33 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__33> : tensor<2048xf32>} : () -> !torch.vtensor<[2048],f32> 
    %34 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__34> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %35 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__35> : tensor<1x2048x1x1xf32>} : () -> !torch.vtensor<[1,2048,1,1],f32> 
    %36 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__36> : tensor<2048xf32>} : () -> !torch.vtensor<[2048],f32> 
    %37 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__37> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %38 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__38> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %39 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__39> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %40 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__40> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %41 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__41> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %42 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__42> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %43 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__43> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %44 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__44> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %45 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__45> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %46 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__46> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %47 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__47> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %48 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__48> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %49 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__49> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %50 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__50> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %51 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__51> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %52 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__52> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %53 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__53> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %54 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__54> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %55 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__55> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %56 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__56> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %57 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__57> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %58 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__58> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %59 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__59> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %60 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__60> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %61 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__61> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %62 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__62> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %63 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__63> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %64 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__64> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %65 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__65> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %66 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__66> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %67 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__67> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %68 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__68> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %69 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__69> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %70 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__70> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %71 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__71> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %72 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__72> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %73 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__73> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %74 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__74> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %75 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__75> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %76 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__76> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %77 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__77> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %78 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__78> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %79 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__79> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %80 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__80> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %81 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__81> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %82 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__82> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %83 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__83> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %84 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__84> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %85 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__85> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %86 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__86> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %87 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__87> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %88 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__88> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %89 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__89> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %90 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__90> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %91 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__91> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %92 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__92> : tensor<1x1024x1x1xf32>} : () -> !torch.vtensor<[1,1024,1,1],f32> 
    %93 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__93> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %94 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__94> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %95 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__95> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %96 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__96> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %97 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__97> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %98 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__98> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %99 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__99> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %100 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__100> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %101 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__101> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %102 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__102> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %103 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__103> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %104 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__104> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %105 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__105> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %106 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__106> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %107 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__107> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %108 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__108> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %109 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__109> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %110 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__110> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %111 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__111> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %112 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__112> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %113 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__113> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %114 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__114> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %115 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__115> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %116 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__116> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %117 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__117> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %118 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__118> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %119 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__119> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %120 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__120> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %121 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__121> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %122 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__122> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %123 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__123> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %124 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__124> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %125 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__125> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %126 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__126> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %127 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__127> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %128 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__128> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %129 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__129> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %130 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__130> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %131 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__131> : tensor<1x512x1x1xf32>} : () -> !torch.vtensor<[1,512,1,1],f32> 
    %132 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__132> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %133 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__133> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %134 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__134> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %135 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__135> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %136 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__136> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %137 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__137> : tensor<1x128x1x1xf32>} : () -> !torch.vtensor<[1,128,1,1],f32> 
    %138 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__138> : tensor<128xf32>} : () -> !torch.vtensor<[128],f32> 
    %139 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__139> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %140 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__140> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %141 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__141> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %142 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__142> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %143 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__143> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %144 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__144> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> 
    %145 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__145> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %146 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__146> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %147 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__147> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> 
    %148 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__148> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %149 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__149> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %150 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__150> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %151 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__151> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %152 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__152> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %153 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__153> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> 
    %154 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__154> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %155 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__155> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %156 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__156> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> 
    %157 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__157> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %158 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__158> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %159 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__159> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %160 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__160> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %161 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__161> : tensor<1x256x1x1xf32>} : () -> !torch.vtensor<[1,256,1,1],f32> 
    %162 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__162> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %163 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__163> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %164 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__164> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %165 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__165> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> 
    %166 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__166> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %167 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__167> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %168 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__168> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> 
    %169 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__169> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %170 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__170> : tensor<1x64x1x1xf32>} : () -> !torch.vtensor<[1,64,1,1],f32> 
    %171 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__171> : tensor<64xf32>} : () -> !torch.vtensor<[64],f32> 
    %172 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__172> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %173 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__173> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %174 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__174> : tensor<3x1x1xf32>} : () -> !torch.vtensor<[3,1,1],f32> 
    %175 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__175> : tensor<3x1x1xf32>} : () -> !torch.vtensor<[3,1,1],f32> 
    %176 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__176> : tensor<64x3x7x7xf32>} : () -> !torch.vtensor<[64,3,7,7],f32> 
    %177 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__177> : tensor<64x64x1x1xf32>} : () -> !torch.vtensor<[64,64,1,1],f32> 
    %178 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__178> : tensor<64x64x3x3xf32>} : () -> !torch.vtensor<[64,64,3,3],f32> 
    %179 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__179> : tensor<256x64x1x1xf32>} : () -> !torch.vtensor<[256,64,1,1],f32> 
    %180 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__180> : tensor<256x64x1x1xf32>} : () -> !torch.vtensor<[256,64,1,1],f32> 
    %181 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__181> : tensor<64x256x1x1xf32>} : () -> !torch.vtensor<[64,256,1,1],f32> 
    %182 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__182> : tensor<64x64x3x3xf32>} : () -> !torch.vtensor<[64,64,3,3],f32> 
    %183 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__183> : tensor<256x64x1x1xf32>} : () -> !torch.vtensor<[256,64,1,1],f32> 
    %184 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__184> : tensor<64x256x1x1xf32>} : () -> !torch.vtensor<[64,256,1,1],f32> 
    %185 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__185> : tensor<64x64x3x3xf32>} : () -> !torch.vtensor<[64,64,3,3],f32> 
    %186 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__186> : tensor<256x64x1x1xf32>} : () -> !torch.vtensor<[256,64,1,1],f32> 
    %187 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__187> : tensor<128x256x1x1xf32>} : () -> !torch.vtensor<[128,256,1,1],f32> 
    %188 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__188> : tensor<128x128x3x3xf32>} : () -> !torch.vtensor<[128,128,3,3],f32> 
    %189 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__189> : tensor<512x128x1x1xf32>} : () -> !torch.vtensor<[512,128,1,1],f32> 
    %190 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__190> : tensor<512x256x1x1xf32>} : () -> !torch.vtensor<[512,256,1,1],f32> 
    %191 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__191> : tensor<128x512x1x1xf32>} : () -> !torch.vtensor<[128,512,1,1],f32> 
    %192 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__192> : tensor<128x128x3x3xf32>} : () -> !torch.vtensor<[128,128,3,3],f32> 
    %193 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__193> : tensor<512x128x1x1xf32>} : () -> !torch.vtensor<[512,128,1,1],f32> 
    %194 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__194> : tensor<128x512x1x1xf32>} : () -> !torch.vtensor<[128,512,1,1],f32> 
    %195 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__195> : tensor<128x128x3x3xf32>} : () -> !torch.vtensor<[128,128,3,3],f32> 
    %196 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__196> : tensor<512x128x1x1xf32>} : () -> !torch.vtensor<[512,128,1,1],f32> 
    %197 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__197> : tensor<128x512x1x1xf32>} : () -> !torch.vtensor<[128,512,1,1],f32> 
    %198 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__198> : tensor<128x128x3x3xf32>} : () -> !torch.vtensor<[128,128,3,3],f32> 
    %199 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__199> : tensor<512x128x1x1xf32>} : () -> !torch.vtensor<[512,128,1,1],f32> 
    %200 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__200> : tensor<256x512x1x1xf32>} : () -> !torch.vtensor<[256,512,1,1],f32> 
    %201 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__201> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %202 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__202> : tensor<1024x256x1x1xf32>} : () -> !torch.vtensor<[1024,256,1,1],f32> 
    %203 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__203> : tensor<1024x512x1x1xf32>} : () -> !torch.vtensor<[1024,512,1,1],f32> 
    %204 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__204> : tensor<256x1024x1x1xf32>} : () -> !torch.vtensor<[256,1024,1,1],f32> 
    %205 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__205> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %206 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__206> : tensor<1024x256x1x1xf32>} : () -> !torch.vtensor<[1024,256,1,1],f32> 
    %207 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__207> : tensor<256x1024x1x1xf32>} : () -> !torch.vtensor<[256,1024,1,1],f32> 
    %208 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__208> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %209 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__209> : tensor<1024x256x1x1xf32>} : () -> !torch.vtensor<[1024,256,1,1],f32> 
    %210 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__210> : tensor<256x1024x1x1xf32>} : () -> !torch.vtensor<[256,1024,1,1],f32> 
    %211 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__211> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %212 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__212> : tensor<1024x256x1x1xf32>} : () -> !torch.vtensor<[1024,256,1,1],f32> 
    %213 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__213> : tensor<256x1024x1x1xf32>} : () -> !torch.vtensor<[256,1024,1,1],f32> 
    %214 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__214> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %215 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__215> : tensor<1024x256x1x1xf32>} : () -> !torch.vtensor<[1024,256,1,1],f32> 
    %216 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__216> : tensor<256x1024x1x1xf32>} : () -> !torch.vtensor<[256,1024,1,1],f32> 
    %217 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__217> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %218 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__218> : tensor<1024x256x1x1xf32>} : () -> !torch.vtensor<[1024,256,1,1],f32> 
    %219 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__219> : tensor<512x1024x1x1xf32>} : () -> !torch.vtensor<[512,1024,1,1],f32> 
    %220 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__220> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %221 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__221> : tensor<2048x512x1x1xf32>} : () -> !torch.vtensor<[2048,512,1,1],f32> 
    %222 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__222> : tensor<2048x1024x1x1xf32>} : () -> !torch.vtensor<[2048,1024,1,1],f32> 
    %223 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__223> : tensor<512x2048x1x1xf32>} : () -> !torch.vtensor<[512,2048,1,1],f32> 
    %224 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__224> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %225 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__225> : tensor<2048x512x1x1xf32>} : () -> !torch.vtensor<[2048,512,1,1],f32> 
    %226 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__226> : tensor<512x2048x1x1xf32>} : () -> !torch.vtensor<[512,2048,1,1],f32> 
    %227 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__227> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %228 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__228> : tensor<2048x512x1x1xf32>} : () -> !torch.vtensor<[2048,512,1,1],f32> 
    %229 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__229> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %230 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__230> : tensor<256x2048x1x1xf32>} : () -> !torch.vtensor<[256,2048,1,1],f32> 
    %231 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__231> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %232 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__232> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %233 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__233> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %234 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__234> : tensor<256x1024x1x1xf32>} : () -> !torch.vtensor<[256,1024,1,1],f32> 
    %235 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__235> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %236 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__236> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %237 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__237> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %238 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__238> : tensor<256x512x1x1xf32>} : () -> !torch.vtensor<[256,512,1,1],f32> 
    %239 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__239> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %240 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__240> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %241 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__241> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %242 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__242> : tensor<256x256x1x1xf32>} : () -> !torch.vtensor<[256,256,1,1],f32> 
    %243 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__243> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %244 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__244> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %245 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__245> : tensor<256xf32>} : () -> !torch.vtensor<[256],f32> 
    %246 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__246> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %247 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__247> : tensor<3xf32>} : () -> !torch.vtensor<[3],f32> 
    %248 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__248> : tensor<3x256x1x1xf32>} : () -> !torch.vtensor<[3,256,1,1],f32> 
    %249 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__249> : tensor<12xf32>} : () -> !torch.vtensor<[12],f32> 
    %250 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__250> : tensor<12x256x1x1xf32>} : () -> !torch.vtensor<[12,256,1,1],f32> 
    %251 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__251> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %252 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__252> : tensor<1024x12544xf32>} : () -> !torch.vtensor<[1024,12544],f32> 
    %253 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__253> : tensor<1024xf32>} : () -> !torch.vtensor<[1024],f32> 
    %254 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__254> : tensor<1024x1024xf32>} : () -> !torch.vtensor<[1024,1024],f32> 
    %255 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__255> : tensor<2xf32>} : () -> !torch.vtensor<[2],f32> 
    %256 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__256> : tensor<2x1024xf32>} : () -> !torch.vtensor<[2,1024],f32> 
    %257 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__257> : tensor<8xf32>} : () -> !torch.vtensor<[8],f32> 
    %258 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__258> : tensor<8x1024xf32>} : () -> !torch.vtensor<[8,1024],f32> 
    %259 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__259> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %260 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__260> : tensor<512x256x3x3xf32>} : () -> !torch.vtensor<[512,256,3,3],f32> 
    %261 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__261> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %262 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__262> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %263 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__263> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %264 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__264> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %265 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__265> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %266 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__266> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %267 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__267> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %268 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__268> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %269 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__269> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %270 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__270> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %271 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__271> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %272 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__272> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %273 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__273> : tensor<512xf32>} : () -> !torch.vtensor<[512],f32> 
    %274 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__274> : tensor<512x512x3x3xf32>} : () -> !torch.vtensor<[512,512,3,3],f32> 
    %275 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__275> : tensor<17xf32>} : () -> !torch.vtensor<[17],f32> 
    %276 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__276> : tensor<512x17x4x4xf32>} : () -> !torch.vtensor<[512,17,4,4],f32> 
    %277 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__277> : tensor<i1>} : () -> !torch.vtensor<[],i1> 
    %278 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__278> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %279 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__279> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %280 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__280> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %281 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__281> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %282 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__282> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %283 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__283> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %284 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__284> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %285 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__285> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %286 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__286> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %287 = torch.operator "onnx.QuantizeLinear"(%arg1, %285, %286) : (!torch.vtensor<[3,500,400],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,500,400],si8> 
    %288 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__287> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %289 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__288> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %290 = torch.operator "onnx.DequantizeLinear"(%287, %288, %289) : (!torch.vtensor<[3,500,400],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,500,400],f32> 
    %291 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__289> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %292 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__290> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %293 = torch.operator "onnx.QuantizeLinear"(%arg0, %291, %292) : (!torch.vtensor<[3,300,400],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,300,400],si8> 
    %294 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__291> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %295 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__292> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %296 = torch.operator "onnx.DequantizeLinear"(%293, %294, %295) : (!torch.vtensor<[3,300,400],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,300,400],f32> 
    %297 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__293> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %298 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__294> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %299 = torch.operator "onnx.QuantizeLinear"(%280, %297, %298) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %300 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__295> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %301 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__296> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %302 = torch.operator "onnx.DequantizeLinear"(%299, %300, %301) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %303 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__297> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %304 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__298> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %305 = torch.operator "onnx.QuantizeLinear"(%276, %303, %304) : (!torch.vtensor<[512,17,4,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,17,4,4],si8> 
    %306 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__299> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %307 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__300> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %308 = torch.operator "onnx.DequantizeLinear"(%305, %306, %307) : (!torch.vtensor<[512,17,4,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,17,4,4],f32> 
    %309 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__301> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %310 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__302> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %311 = torch.operator "onnx.QuantizeLinear"(%275, %309, %310) : (!torch.vtensor<[17],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[17],si8> 
    %312 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__303> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %313 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__304> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %314 = torch.operator "onnx.DequantizeLinear"(%311, %312, %313) : (!torch.vtensor<[17],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[17],f32> 
    %315 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__305> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %316 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__306> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %317 = torch.operator "onnx.QuantizeLinear"(%274, %315, %316) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %318 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__307> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %319 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__308> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %320 = torch.operator "onnx.DequantizeLinear"(%317, %318, %319) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %321 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__309> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %322 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__310> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %323 = torch.operator "onnx.QuantizeLinear"(%273, %321, %322) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %324 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__311> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %325 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__312> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %326 = torch.operator "onnx.DequantizeLinear"(%323, %324, %325) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %327 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__313> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %328 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__314> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %329 = torch.operator "onnx.QuantizeLinear"(%272, %327, %328) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %330 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__315> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %331 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__316> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %332 = torch.operator "onnx.DequantizeLinear"(%329, %330, %331) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %333 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__317> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %334 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__318> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %335 = torch.operator "onnx.QuantizeLinear"(%271, %333, %334) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %336 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__319> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %337 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__320> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %338 = torch.operator "onnx.DequantizeLinear"(%335, %336, %337) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %339 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__321> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %340 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__322> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %341 = torch.operator "onnx.QuantizeLinear"(%270, %339, %340) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %342 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__323> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %343 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__324> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %344 = torch.operator "onnx.DequantizeLinear"(%341, %342, %343) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %345 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__325> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %346 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__326> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %347 = torch.operator "onnx.QuantizeLinear"(%269, %345, %346) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %348 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__327> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %349 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__328> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %350 = torch.operator "onnx.DequantizeLinear"(%347, %348, %349) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %351 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__329> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %352 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__330> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %353 = torch.operator "onnx.QuantizeLinear"(%268, %351, %352) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %354 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__331> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %355 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__332> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %356 = torch.operator "onnx.DequantizeLinear"(%353, %354, %355) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %357 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__333> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %358 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__334> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %359 = torch.operator "onnx.QuantizeLinear"(%267, %357, %358) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %360 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__335> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %361 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__336> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %362 = torch.operator "onnx.DequantizeLinear"(%359, %360, %361) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %363 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__337> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %364 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__338> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %365 = torch.operator "onnx.QuantizeLinear"(%266, %363, %364) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %366 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__339> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %367 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__340> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %368 = torch.operator "onnx.DequantizeLinear"(%365, %366, %367) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %369 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__341> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %370 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__342> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %371 = torch.operator "onnx.QuantizeLinear"(%265, %369, %370) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %372 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__343> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %373 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__344> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %374 = torch.operator "onnx.DequantizeLinear"(%371, %372, %373) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %375 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__345> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %376 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__346> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %377 = torch.operator "onnx.QuantizeLinear"(%264, %375, %376) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %378 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__347> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %379 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__348> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %380 = torch.operator "onnx.DequantizeLinear"(%377, %378, %379) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %381 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__349> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %382 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__350> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %383 = torch.operator "onnx.QuantizeLinear"(%263, %381, %382) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %384 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__351> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %385 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__352> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %386 = torch.operator "onnx.DequantizeLinear"(%383, %384, %385) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %387 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__353> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %388 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__354> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %389 = torch.operator "onnx.QuantizeLinear"(%262, %387, %388) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %390 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__355> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %391 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__356> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %392 = torch.operator "onnx.DequantizeLinear"(%389, %390, %391) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %393 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__357> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %394 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__358> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %395 = torch.operator "onnx.QuantizeLinear"(%261, %393, %394) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %396 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__359> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %397 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__360> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %398 = torch.operator "onnx.DequantizeLinear"(%395, %396, %397) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %399 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__361> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %400 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__362> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %401 = torch.operator "onnx.QuantizeLinear"(%260, %399, %400) : (!torch.vtensor<[512,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,256,3,3],si8> 
    %402 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__363> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %403 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__364> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %404 = torch.operator "onnx.DequantizeLinear"(%401, %402, %403) : (!torch.vtensor<[512,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,256,3,3],f32> 
    %405 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__365> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %406 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__366> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %407 = torch.operator "onnx.QuantizeLinear"(%259, %405, %406) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %408 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__367> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %409 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__368> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %410 = torch.operator "onnx.DequantizeLinear"(%407, %408, %409) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %411 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__369> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %412 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__370> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %413 = torch.operator "onnx.QuantizeLinear"(%258, %411, %412) : (!torch.vtensor<[8,1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[8,1024],si8> 
    %414 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__371> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %415 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__372> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %416 = torch.operator "onnx.DequantizeLinear"(%413, %414, %415) : (!torch.vtensor<[8,1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[8,1024],f32> 
    %417 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__373> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %418 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__374> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %419 = torch.operator "onnx.QuantizeLinear"(%257, %417, %418) : (!torch.vtensor<[8],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[8],si8> 
    %420 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__375> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %421 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__376> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %422 = torch.operator "onnx.DequantizeLinear"(%419, %420, %421) : (!torch.vtensor<[8],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[8],f32> 
    %423 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__377> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %424 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__378> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %425 = torch.operator "onnx.QuantizeLinear"(%256, %423, %424) : (!torch.vtensor<[2,1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024],si8> 
    %426 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__379> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %427 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__380> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %428 = torch.operator "onnx.DequantizeLinear"(%425, %426, %427) : (!torch.vtensor<[2,1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024],f32> 
    %429 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__381> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %430 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__382> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %431 = torch.operator "onnx.QuantizeLinear"(%255, %429, %430) : (!torch.vtensor<[2],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],si8> 
    %432 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__383> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %433 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__384> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %434 = torch.operator "onnx.DequantizeLinear"(%431, %432, %433) : (!torch.vtensor<[2],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],f32> 
    %435 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__385> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %436 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__386> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %437 = torch.operator "onnx.QuantizeLinear"(%254, %435, %436) : (!torch.vtensor<[1024,1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,1024],si8> 
    %438 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__387> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %439 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__388> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %440 = torch.operator "onnx.DequantizeLinear"(%437, %438, %439) : (!torch.vtensor<[1024,1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,1024],f32> 
    %441 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__389> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %442 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__390> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %443 = torch.operator "onnx.QuantizeLinear"(%253, %441, %442) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %444 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__391> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %445 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__392> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %446 = torch.operator "onnx.DequantizeLinear"(%443, %444, %445) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %447 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__393> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %448 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__394> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %449 = torch.operator "onnx.QuantizeLinear"(%252, %447, %448) : (!torch.vtensor<[1024,12544],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,12544],si8> 
    %450 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__395> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %451 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__396> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %452 = torch.operator "onnx.DequantizeLinear"(%449, %450, %451) : (!torch.vtensor<[1024,12544],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,12544],f32> 
    %453 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__397> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %454 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__398> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %455 = torch.operator "onnx.QuantizeLinear"(%251, %453, %454) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %456 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__399> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %457 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__400> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %458 = torch.operator "onnx.DequantizeLinear"(%455, %456, %457) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %459 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__401> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %460 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__402> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %461 = torch.operator "onnx.QuantizeLinear"(%250, %459, %460) : (!torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],si8> 
    %462 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__403> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %463 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__404> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %464 = torch.operator "onnx.DequantizeLinear"(%461, %462, %463) : (!torch.vtensor<[12,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],f32> 
    %465 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__405> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %466 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__406> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %467 = torch.operator "onnx.QuantizeLinear"(%249, %465, %466) : (!torch.vtensor<[12],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],si8> 
    %468 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__407> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %469 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__408> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %470 = torch.operator "onnx.DequantizeLinear"(%467, %468, %469) : (!torch.vtensor<[12],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],f32> 
    %471 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__409> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %472 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__410> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %473 = torch.operator "onnx.QuantizeLinear"(%248, %471, %472) : (!torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],si8> 
    %474 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__411> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %475 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__412> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %476 = torch.operator "onnx.DequantizeLinear"(%473, %474, %475) : (!torch.vtensor<[3,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],f32> 
    %477 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__413> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %478 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__414> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %479 = torch.operator "onnx.QuantizeLinear"(%247, %477, %478) : (!torch.vtensor<[3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],si8> 
    %480 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__415> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %481 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__416> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %482 = torch.operator "onnx.DequantizeLinear"(%479, %480, %481) : (!torch.vtensor<[3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],f32> 
    %483 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__417> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %484 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__418> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %485 = torch.operator "onnx.QuantizeLinear"(%246, %483, %484) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %486 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__419> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %487 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__420> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %488 = torch.operator "onnx.DequantizeLinear"(%485, %486, %487) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %489 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__421> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %490 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__422> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %491 = torch.operator "onnx.QuantizeLinear"(%245, %489, %490) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %492 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__423> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %493 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__424> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %494 = torch.operator "onnx.DequantizeLinear"(%491, %492, %493) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %495 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__425> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %496 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__426> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %497 = torch.operator "onnx.QuantizeLinear"(%244, %495, %496) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %498 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__427> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %499 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__428> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %500 = torch.operator "onnx.DequantizeLinear"(%497, %498, %499) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %501 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__429> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %502 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__430> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %503 = torch.operator "onnx.QuantizeLinear"(%243, %501, %502) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %504 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__431> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %505 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__432> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %506 = torch.operator "onnx.DequantizeLinear"(%503, %504, %505) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %507 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__433> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %508 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__434> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %509 = torch.operator "onnx.QuantizeLinear"(%242, %507, %508) : (!torch.vtensor<[256,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,1,1],si8> 
    %510 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__435> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %511 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__436> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %512 = torch.operator "onnx.DequantizeLinear"(%509, %510, %511) : (!torch.vtensor<[256,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,1,1],f32> 
    %513 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__437> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %514 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__438> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %515 = torch.operator "onnx.QuantizeLinear"(%241, %513, %514) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %516 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__439> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %517 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__440> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %518 = torch.operator "onnx.DequantizeLinear"(%515, %516, %517) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %519 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__441> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %520 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__442> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %521 = torch.operator "onnx.QuantizeLinear"(%240, %519, %520) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %522 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__443> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %523 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__444> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %524 = torch.operator "onnx.DequantizeLinear"(%521, %522, %523) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %525 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__445> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %526 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__446> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %527 = torch.operator "onnx.QuantizeLinear"(%239, %525, %526) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %528 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__447> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %529 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__448> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %530 = torch.operator "onnx.DequantizeLinear"(%527, %528, %529) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %531 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__449> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %532 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__450> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %533 = torch.operator "onnx.QuantizeLinear"(%238, %531, %532) : (!torch.vtensor<[256,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,512,1,1],si8> 
    %534 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__451> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %535 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__452> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %536 = torch.operator "onnx.DequantizeLinear"(%533, %534, %535) : (!torch.vtensor<[256,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,512,1,1],f32> 
    %537 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__453> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %538 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__454> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %539 = torch.operator "onnx.QuantizeLinear"(%237, %537, %538) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %540 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__455> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %541 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__456> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %542 = torch.operator "onnx.DequantizeLinear"(%539, %540, %541) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %543 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__457> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %544 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__458> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %545 = torch.operator "onnx.QuantizeLinear"(%236, %543, %544) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %546 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__459> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %547 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__460> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %548 = torch.operator "onnx.DequantizeLinear"(%545, %546, %547) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %549 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__461> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %550 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__462> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %551 = torch.operator "onnx.QuantizeLinear"(%235, %549, %550) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %552 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__463> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %553 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__464> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %554 = torch.operator "onnx.DequantizeLinear"(%551, %552, %553) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %555 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__465> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %556 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__466> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %557 = torch.operator "onnx.QuantizeLinear"(%234, %555, %556) : (!torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],si8> 
    %558 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__467> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %559 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__468> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %560 = torch.operator "onnx.DequantizeLinear"(%557, %558, %559) : (!torch.vtensor<[256,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],f32> 
    %561 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__469> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %562 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__470> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %563 = torch.operator "onnx.QuantizeLinear"(%233, %561, %562) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %564 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__471> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %565 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__472> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %566 = torch.operator "onnx.DequantizeLinear"(%563, %564, %565) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %567 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__473> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %568 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__474> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %569 = torch.operator "onnx.QuantizeLinear"(%232, %567, %568) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %570 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__475> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %571 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__476> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %572 = torch.operator "onnx.DequantizeLinear"(%569, %570, %571) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %573 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__477> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %574 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__478> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %575 = torch.operator "onnx.QuantizeLinear"(%231, %573, %574) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %576 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__479> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %577 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__480> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %578 = torch.operator "onnx.DequantizeLinear"(%575, %576, %577) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %579 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__481> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %580 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__482> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %581 = torch.operator "onnx.QuantizeLinear"(%230, %579, %580) : (!torch.vtensor<[256,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,2048,1,1],si8> 
    %582 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__483> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %583 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__484> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %584 = torch.operator "onnx.DequantizeLinear"(%581, %582, %583) : (!torch.vtensor<[256,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,2048,1,1],f32> 
    %585 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__485> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %586 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__486> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %587 = torch.operator "onnx.QuantizeLinear"(%229, %585, %586) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %588 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__487> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %589 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__488> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %590 = torch.operator "onnx.DequantizeLinear"(%587, %588, %589) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %591 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__489> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %592 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__490> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %593 = torch.operator "onnx.QuantizeLinear"(%228, %591, %592) : (!torch.vtensor<[2048,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,512,1,1],si8> 
    %594 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__491> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %595 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__492> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %596 = torch.operator "onnx.DequantizeLinear"(%593, %594, %595) : (!torch.vtensor<[2048,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,512,1,1],f32> 
    %597 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__493> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %598 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__494> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %599 = torch.operator "onnx.QuantizeLinear"(%227, %597, %598) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %600 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__495> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %601 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__496> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %602 = torch.operator "onnx.DequantizeLinear"(%599, %600, %601) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %603 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__497> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %604 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__498> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %605 = torch.operator "onnx.QuantizeLinear"(%226, %603, %604) : (!torch.vtensor<[512,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,2048,1,1],si8> 
    %606 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__499> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %607 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__500> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %608 = torch.operator "onnx.DequantizeLinear"(%605, %606, %607) : (!torch.vtensor<[512,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,2048,1,1],f32> 
    %609 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__501> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %610 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__502> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %611 = torch.operator "onnx.QuantizeLinear"(%225, %609, %610) : (!torch.vtensor<[2048,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,512,1,1],si8> 
    %612 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__503> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %613 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__504> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %614 = torch.operator "onnx.DequantizeLinear"(%611, %612, %613) : (!torch.vtensor<[2048,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,512,1,1],f32> 
    %615 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__505> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %616 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__506> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %617 = torch.operator "onnx.QuantizeLinear"(%224, %615, %616) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %618 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__507> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %619 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__508> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %620 = torch.operator "onnx.DequantizeLinear"(%617, %618, %619) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %621 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__509> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %622 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__510> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %623 = torch.operator "onnx.QuantizeLinear"(%223, %621, %622) : (!torch.vtensor<[512,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,2048,1,1],si8> 
    %624 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__511> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %625 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__512> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %626 = torch.operator "onnx.DequantizeLinear"(%623, %624, %625) : (!torch.vtensor<[512,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,2048,1,1],f32> 
    %627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__513> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %628 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__514> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %629 = torch.operator "onnx.QuantizeLinear"(%222, %627, %628) : (!torch.vtensor<[2048,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,1024,1,1],si8> 
    %630 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__515> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %631 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__516> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %632 = torch.operator "onnx.DequantizeLinear"(%629, %630, %631) : (!torch.vtensor<[2048,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,1024,1,1],f32> 
    %633 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__517> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %634 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__518> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %635 = torch.operator "onnx.QuantizeLinear"(%221, %633, %634) : (!torch.vtensor<[2048,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,512,1,1],si8> 
    %636 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__519> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %637 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__520> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %638 = torch.operator "onnx.DequantizeLinear"(%635, %636, %637) : (!torch.vtensor<[2048,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048,512,1,1],f32> 
    %639 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__521> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %640 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__522> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %641 = torch.operator "onnx.QuantizeLinear"(%220, %639, %640) : (!torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],si8> 
    %642 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__523> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %643 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__524> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %644 = torch.operator "onnx.DequantizeLinear"(%641, %642, %643) : (!torch.vtensor<[512,512,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,512,3,3],f32> 
    %645 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__525> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %646 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__526> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %647 = torch.operator "onnx.QuantizeLinear"(%219, %645, %646) : (!torch.vtensor<[512,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,1024,1,1],si8> 
    %648 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__527> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %649 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__528> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %650 = torch.operator "onnx.DequantizeLinear"(%647, %648, %649) : (!torch.vtensor<[512,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,1024,1,1],f32> 
    %651 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__529> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__530> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %653 = torch.operator "onnx.QuantizeLinear"(%218, %651, %652) : (!torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],si8> 
    %654 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__531> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %655 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__532> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %656 = torch.operator "onnx.DequantizeLinear"(%653, %654, %655) : (!torch.vtensor<[1024,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],f32> 
    %657 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__533> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %658 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__534> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %659 = torch.operator "onnx.QuantizeLinear"(%217, %657, %658) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %660 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__535> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %661 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__536> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %662 = torch.operator "onnx.DequantizeLinear"(%659, %660, %661) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %663 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__537> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %664 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__538> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %665 = torch.operator "onnx.QuantizeLinear"(%216, %663, %664) : (!torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],si8> 
    %666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__539> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %667 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__540> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %668 = torch.operator "onnx.DequantizeLinear"(%665, %666, %667) : (!torch.vtensor<[256,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],f32> 
    %669 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__541> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %670 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__542> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %671 = torch.operator "onnx.QuantizeLinear"(%215, %669, %670) : (!torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],si8> 
    %672 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__543> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %673 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__544> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %674 = torch.operator "onnx.DequantizeLinear"(%671, %672, %673) : (!torch.vtensor<[1024,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],f32> 
    %675 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__545> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %676 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__546> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %677 = torch.operator "onnx.QuantizeLinear"(%214, %675, %676) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %678 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__547> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %679 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__548> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %680 = torch.operator "onnx.DequantizeLinear"(%677, %678, %679) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %681 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__549> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %682 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__550> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %683 = torch.operator "onnx.QuantizeLinear"(%213, %681, %682) : (!torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],si8> 
    %684 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__551> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %685 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__552> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %686 = torch.operator "onnx.DequantizeLinear"(%683, %684, %685) : (!torch.vtensor<[256,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],f32> 
    %687 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__553> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %688 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__554> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %689 = torch.operator "onnx.QuantizeLinear"(%212, %687, %688) : (!torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],si8> 
    %690 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__555> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %691 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__556> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %692 = torch.operator "onnx.DequantizeLinear"(%689, %690, %691) : (!torch.vtensor<[1024,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],f32> 
    %693 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__557> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %694 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__558> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %695 = torch.operator "onnx.QuantizeLinear"(%211, %693, %694) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %696 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__559> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %697 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__560> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %698 = torch.operator "onnx.DequantizeLinear"(%695, %696, %697) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %699 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__561> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %700 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__562> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %701 = torch.operator "onnx.QuantizeLinear"(%210, %699, %700) : (!torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],si8> 
    %702 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__563> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %703 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__564> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %704 = torch.operator "onnx.DequantizeLinear"(%701, %702, %703) : (!torch.vtensor<[256,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],f32> 
    %705 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__565> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %706 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__566> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %707 = torch.operator "onnx.QuantizeLinear"(%209, %705, %706) : (!torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],si8> 
    %708 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__567> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %709 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__568> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %710 = torch.operator "onnx.DequantizeLinear"(%707, %708, %709) : (!torch.vtensor<[1024,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],f32> 
    %711 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__569> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %712 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__570> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %713 = torch.operator "onnx.QuantizeLinear"(%208, %711, %712) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %714 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__571> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %715 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__572> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %716 = torch.operator "onnx.DequantizeLinear"(%713, %714, %715) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %717 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__573> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %718 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__574> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %719 = torch.operator "onnx.QuantizeLinear"(%207, %717, %718) : (!torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],si8> 
    %720 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__575> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %721 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__576> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %722 = torch.operator "onnx.DequantizeLinear"(%719, %720, %721) : (!torch.vtensor<[256,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],f32> 
    %723 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__577> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %724 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__578> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %725 = torch.operator "onnx.QuantizeLinear"(%206, %723, %724) : (!torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],si8> 
    %726 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__579> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %727 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__580> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %728 = torch.operator "onnx.DequantizeLinear"(%725, %726, %727) : (!torch.vtensor<[1024,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],f32> 
    %729 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__581> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %730 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__582> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %731 = torch.operator "onnx.QuantizeLinear"(%205, %729, %730) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %732 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__583> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %733 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__584> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %734 = torch.operator "onnx.DequantizeLinear"(%731, %732, %733) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %735 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__585> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %736 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__586> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %737 = torch.operator "onnx.QuantizeLinear"(%204, %735, %736) : (!torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],si8> 
    %738 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__587> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %739 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__588> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %740 = torch.operator "onnx.DequantizeLinear"(%737, %738, %739) : (!torch.vtensor<[256,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,1024,1,1],f32> 
    %741 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__589> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %742 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__590> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %743 = torch.operator "onnx.QuantizeLinear"(%203, %741, %742) : (!torch.vtensor<[1024,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,512,1,1],si8> 
    %744 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__591> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %745 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__592> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %746 = torch.operator "onnx.DequantizeLinear"(%743, %744, %745) : (!torch.vtensor<[1024,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,512,1,1],f32> 
    %747 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__593> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %748 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__594> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %749 = torch.operator "onnx.QuantizeLinear"(%202, %747, %748) : (!torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],si8> 
    %750 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__595> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %751 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__596> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %752 = torch.operator "onnx.DequantizeLinear"(%749, %750, %751) : (!torch.vtensor<[1024,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024,256,1,1],f32> 
    %753 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__597> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %754 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__598> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %755 = torch.operator "onnx.QuantizeLinear"(%201, %753, %754) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %756 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__599> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %757 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__600> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %758 = torch.operator "onnx.DequantizeLinear"(%755, %756, %757) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %759 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__601> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %760 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__602> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %761 = torch.operator "onnx.QuantizeLinear"(%200, %759, %760) : (!torch.vtensor<[256,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,512,1,1],si8> 
    %762 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__603> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %763 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__604> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %764 = torch.operator "onnx.DequantizeLinear"(%761, %762, %763) : (!torch.vtensor<[256,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,512,1,1],f32> 
    %765 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__605> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %766 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__606> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %767 = torch.operator "onnx.QuantizeLinear"(%199, %765, %766) : (!torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],si8> 
    %768 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__607> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %769 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__608> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %770 = torch.operator "onnx.DequantizeLinear"(%767, %768, %769) : (!torch.vtensor<[512,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],f32> 
    %771 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__609> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %772 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__610> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %773 = torch.operator "onnx.QuantizeLinear"(%198, %771, %772) : (!torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],si8> 
    %774 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__611> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %775 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__612> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %776 = torch.operator "onnx.DequantizeLinear"(%773, %774, %775) : (!torch.vtensor<[128,128,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],f32> 
    %777 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__613> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %778 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__614> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %779 = torch.operator "onnx.QuantizeLinear"(%197, %777, %778) : (!torch.vtensor<[128,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,512,1,1],si8> 
    %780 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__615> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %781 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__616> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %782 = torch.operator "onnx.DequantizeLinear"(%779, %780, %781) : (!torch.vtensor<[128,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,512,1,1],f32> 
    %783 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__617> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %784 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__618> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %785 = torch.operator "onnx.QuantizeLinear"(%196, %783, %784) : (!torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],si8> 
    %786 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__619> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %787 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__620> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %788 = torch.operator "onnx.DequantizeLinear"(%785, %786, %787) : (!torch.vtensor<[512,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],f32> 
    %789 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__621> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %790 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__622> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %791 = torch.operator "onnx.QuantizeLinear"(%195, %789, %790) : (!torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],si8> 
    %792 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__623> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %793 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__624> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %794 = torch.operator "onnx.DequantizeLinear"(%791, %792, %793) : (!torch.vtensor<[128,128,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],f32> 
    %795 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__625> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %796 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__626> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %797 = torch.operator "onnx.QuantizeLinear"(%194, %795, %796) : (!torch.vtensor<[128,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,512,1,1],si8> 
    %798 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__627> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %799 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__628> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %800 = torch.operator "onnx.DequantizeLinear"(%797, %798, %799) : (!torch.vtensor<[128,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,512,1,1],f32> 
    %801 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__629> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %802 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__630> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %803 = torch.operator "onnx.QuantizeLinear"(%193, %801, %802) : (!torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],si8> 
    %804 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__631> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %805 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__632> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %806 = torch.operator "onnx.DequantizeLinear"(%803, %804, %805) : (!torch.vtensor<[512,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],f32> 
    %807 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__633> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %808 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__634> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %809 = torch.operator "onnx.QuantizeLinear"(%192, %807, %808) : (!torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],si8> 
    %810 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__635> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %811 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__636> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %812 = torch.operator "onnx.DequantizeLinear"(%809, %810, %811) : (!torch.vtensor<[128,128,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],f32> 
    %813 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__637> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %814 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__638> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %815 = torch.operator "onnx.QuantizeLinear"(%191, %813, %814) : (!torch.vtensor<[128,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,512,1,1],si8> 
    %816 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__639> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %817 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__640> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %818 = torch.operator "onnx.DequantizeLinear"(%815, %816, %817) : (!torch.vtensor<[128,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,512,1,1],f32> 
    %819 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__641> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %820 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__642> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %821 = torch.operator "onnx.QuantizeLinear"(%190, %819, %820) : (!torch.vtensor<[512,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,256,1,1],si8> 
    %822 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__643> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %823 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__644> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %824 = torch.operator "onnx.DequantizeLinear"(%821, %822, %823) : (!torch.vtensor<[512,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,256,1,1],f32> 
    %825 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__645> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %826 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__646> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %827 = torch.operator "onnx.QuantizeLinear"(%189, %825, %826) : (!torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],si8> 
    %828 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__647> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %829 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__648> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %830 = torch.operator "onnx.DequantizeLinear"(%827, %828, %829) : (!torch.vtensor<[512,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512,128,1,1],f32> 
    %831 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__649> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %832 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__650> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %833 = torch.operator "onnx.QuantizeLinear"(%188, %831, %832) : (!torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],si8> 
    %834 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__651> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %835 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__652> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %836 = torch.operator "onnx.DequantizeLinear"(%833, %834, %835) : (!torch.vtensor<[128,128,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,128,3,3],f32> 
    %837 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__653> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %838 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__654> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %839 = torch.operator "onnx.QuantizeLinear"(%187, %837, %838) : (!torch.vtensor<[128,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,256,1,1],si8> 
    %840 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__655> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %841 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__656> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %842 = torch.operator "onnx.DequantizeLinear"(%839, %840, %841) : (!torch.vtensor<[128,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128,256,1,1],f32> 
    %843 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__657> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %844 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__658> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %845 = torch.operator "onnx.QuantizeLinear"(%186, %843, %844) : (!torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],si8> 
    %846 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__659> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %847 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__660> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %848 = torch.operator "onnx.DequantizeLinear"(%845, %846, %847) : (!torch.vtensor<[256,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],f32> 
    %849 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__661> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %850 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__662> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %851 = torch.operator "onnx.QuantizeLinear"(%185, %849, %850) : (!torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,3,3],si8> 
    %852 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__663> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %853 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__664> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %854 = torch.operator "onnx.DequantizeLinear"(%851, %852, %853) : (!torch.vtensor<[64,64,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,3,3],f32> 
    %855 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__665> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %856 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__666> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %857 = torch.operator "onnx.QuantizeLinear"(%184, %855, %856) : (!torch.vtensor<[64,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,256,1,1],si8> 
    %858 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__667> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %859 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__668> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %860 = torch.operator "onnx.DequantizeLinear"(%857, %858, %859) : (!torch.vtensor<[64,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,256,1,1],f32> 
    %861 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__669> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %862 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__670> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %863 = torch.operator "onnx.QuantizeLinear"(%183, %861, %862) : (!torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],si8> 
    %864 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__671> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %865 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__672> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %866 = torch.operator "onnx.DequantizeLinear"(%863, %864, %865) : (!torch.vtensor<[256,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],f32> 
    %867 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__673> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %868 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__674> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %869 = torch.operator "onnx.QuantizeLinear"(%182, %867, %868) : (!torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,3,3],si8> 
    %870 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__675> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %871 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__676> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %872 = torch.operator "onnx.DequantizeLinear"(%869, %870, %871) : (!torch.vtensor<[64,64,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,3,3],f32> 
    %873 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__677> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %874 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__678> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %875 = torch.operator "onnx.QuantizeLinear"(%181, %873, %874) : (!torch.vtensor<[64,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,256,1,1],si8> 
    %876 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__679> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %877 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__680> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %878 = torch.operator "onnx.DequantizeLinear"(%875, %876, %877) : (!torch.vtensor<[64,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,256,1,1],f32> 
    %879 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__681> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %880 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__682> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %881 = torch.operator "onnx.QuantizeLinear"(%180, %879, %880) : (!torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],si8> 
    %882 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__683> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %883 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__684> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %884 = torch.operator "onnx.DequantizeLinear"(%881, %882, %883) : (!torch.vtensor<[256,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],f32> 
    %885 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__685> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %886 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__686> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %887 = torch.operator "onnx.QuantizeLinear"(%179, %885, %886) : (!torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],si8> 
    %888 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__687> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %889 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__688> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %890 = torch.operator "onnx.DequantizeLinear"(%887, %888, %889) : (!torch.vtensor<[256,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,64,1,1],f32> 
    %891 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__689> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %892 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__690> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %893 = torch.operator "onnx.QuantizeLinear"(%178, %891, %892) : (!torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,3,3],si8> 
    %894 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__691> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %895 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__692> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %896 = torch.operator "onnx.DequantizeLinear"(%893, %894, %895) : (!torch.vtensor<[64,64,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,3,3],f32> 
    %897 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__693> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %898 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__694> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %899 = torch.operator "onnx.QuantizeLinear"(%177, %897, %898) : (!torch.vtensor<[64,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,1,1],si8> 
    %900 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__695> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %901 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__696> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %902 = torch.operator "onnx.DequantizeLinear"(%899, %900, %901) : (!torch.vtensor<[64,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,64,1,1],f32> 
    %903 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__697> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %904 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__698> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %905 = torch.operator "onnx.QuantizeLinear"(%176, %903, %904) : (!torch.vtensor<[64,3,7,7],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,3,7,7],si8> 
    %906 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__699> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %907 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__700> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %908 = torch.operator "onnx.DequantizeLinear"(%905, %906, %907) : (!torch.vtensor<[64,3,7,7],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64,3,7,7],f32> 
    %909 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__701> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %910 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__702> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %911 = torch.operator "onnx.QuantizeLinear"(%175, %909, %910) : (!torch.vtensor<[3,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],si8> 
    %912 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__703> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %913 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__704> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %914 = torch.operator "onnx.DequantizeLinear"(%911, %912, %913) : (!torch.vtensor<[3,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],f32> 
    %915 = torch.operator "onnx.Sub"(%296, %914) : (!torch.vtensor<[3,300,400],f32>, !torch.vtensor<[3,1,1],f32>) -> !torch.vtensor<[3,300,400],f32> 
    %916 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__705> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %917 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__706> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %918 = torch.operator "onnx.QuantizeLinear"(%915, %916, %917) : (!torch.vtensor<[3,300,400],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,300,400],si8> 
    %919 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__707> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %920 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__708> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %921 = torch.operator "onnx.DequantizeLinear"(%918, %919, %920) : (!torch.vtensor<[3,300,400],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,300,400],f32> 
    %922 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__709> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %923 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__710> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %924 = torch.operator "onnx.QuantizeLinear"(%174, %922, %923) : (!torch.vtensor<[3,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],si8> 
    %925 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__711> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %926 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__712> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %927 = torch.operator "onnx.DequantizeLinear"(%924, %925, %926) : (!torch.vtensor<[3,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],f32> 
    %928 = torch.operator "onnx.Div"(%921, %927) : (!torch.vtensor<[3,300,400],f32>, !torch.vtensor<[3,1,1],f32>) -> !torch.vtensor<[3,300,400],f32> 
    %929 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__713> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %930 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__714> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %931 = torch.operator "onnx.QuantizeLinear"(%928, %929, %930) : (!torch.vtensor<[3,300,400],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,300,400],si8> 
    %932 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__715> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %933 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__716> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %934 = torch.operator "onnx.DequantizeLinear"(%931, %932, %933) : (!torch.vtensor<[3,300,400],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,300,400],f32> 
    %935 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__717> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %936 = torch.operator "onnx.ReduceMin"(%935) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %937 = torch.operator "onnx.Cast"(%936) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %938 = torch.operator "onnx.ReduceMax"(%935) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %939 = torch.operator "onnx.Cast"(%938) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %940 = torch.operator "onnx.Reciprocal"(%937) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %941 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__718> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %942 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__719> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %943 = torch.operator "onnx.QuantizeLinear"(%940, %941, %942) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %944 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__720> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %945 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__721> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %946 = torch.operator "onnx.DequantizeLinear"(%943, %944, %945) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %947 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__722> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %948 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__723> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %949 = torch.operator "onnx.QuantizeLinear"(%173, %947, %948) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %950 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__724> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %951 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__725> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %952 = torch.operator "onnx.DequantizeLinear"(%949, %950, %951) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %953 = torch.operator "onnx.Mul"(%946, %952) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %954 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__726> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %955 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__727> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %956 = torch.operator "onnx.QuantizeLinear"(%953, %954, %955) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %957 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__728> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %958 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__729> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %959 = torch.operator "onnx.DequantizeLinear"(%956, %957, %958) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %960 = torch.operator "onnx.Reciprocal"(%939) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %961 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__730> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %962 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__731> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %963 = torch.operator "onnx.QuantizeLinear"(%960, %961, %962) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %964 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__732> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %965 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__733> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %966 = torch.operator "onnx.DequantizeLinear"(%963, %964, %965) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %967 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__734> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %968 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__735> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %969 = torch.operator "onnx.QuantizeLinear"(%172, %967, %968) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %970 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__736> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %971 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__737> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %972 = torch.operator "onnx.DequantizeLinear"(%969, %970, %971) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %973 = torch.operator "onnx.Mul"(%966, %972) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %974 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__738> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %975 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__739> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %976 = torch.operator "onnx.QuantizeLinear"(%973, %974, %975) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %977 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__740> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %978 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__741> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %979 = torch.operator "onnx.DequantizeLinear"(%976, %977, %978) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %980 = torch.operator "onnx.Min"(%959, %979) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %981 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__742> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %982 = torch.operator "onnx.Unsqueeze"(%934, %981) : (!torch.vtensor<[3,300,400],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,3,300,400],f32> 
    %983 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__743> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %984 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__744> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %985 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__745> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %986 = torch.operator "onnx.QuantizeLinear"(%983, %984, %985) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %987 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__746> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %988 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__747> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %989 = torch.operator "onnx.DequantizeLinear"(%986, %987, %988) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %990 = torch.operator "onnx.Cast"(%980) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %991 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__748> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %992 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__749> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %993 = torch.operator "onnx.QuantizeLinear"(%990, %991, %992) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %994 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__750> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %995 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__751> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %996 = torch.operator "onnx.DequantizeLinear"(%993, %994, %995) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %997 = torch.operator "onnx.Mul"(%989, %996) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %998 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__752> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %999 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__753> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1000 = torch.operator "onnx.QuantizeLinear"(%997, %998, %999) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1001 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__754> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1002 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__755> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1003 = torch.operator "onnx.DequantizeLinear"(%1000, %1001, %1002) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1004 = torch.operator "onnx.Cast"(%1003) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1005 = torch.operator "onnx.Floor"(%1004) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1006 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__756> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1007 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__757> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1008 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__758> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1009 = torch.operator "onnx.QuantizeLinear"(%1006, %1007, %1008) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1010 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__759> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1011 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__760> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1012 = torch.operator "onnx.DequantizeLinear"(%1009, %1010, %1011) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1013 = torch.operator "onnx.Mul"(%1012, %996) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1014 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__761> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1015 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__762> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1016 = torch.operator "onnx.QuantizeLinear"(%1013, %1014, %1015) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1017 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__763> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1018 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__764> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1019 = torch.operator "onnx.DequantizeLinear"(%1016, %1017, %1018) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1020 = torch.operator "onnx.Cast"(%1019) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1021 = torch.operator "onnx.Floor"(%1020) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1022 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__765> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1023 = torch.operator "onnx.Unsqueeze"(%1005, %1022) : (!torch.vtensor<[],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],f32> 
    %1024 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__766> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1025 = torch.operator "onnx.Unsqueeze"(%1021, %1024) : (!torch.vtensor<[],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],f32> 
    %1026 = torch.operator "onnx.Concat"(%1023, %1025) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],f32>, !torch.vtensor<[1],f32>) -> !torch.vtensor<[2],f32> 
    %1027 = torch.operator "onnx.Shape"(%982) : (!torch.vtensor<[1,3,300,400],f32>) -> !torch.vtensor<[4],si64> 
    %1028 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__767> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1029 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__768> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1030 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__769> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1031 = torch.operator "onnx.Slice"(%1027, %1029, %1030, %1028) : (!torch.vtensor<[4],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %1032 = torch.operator "onnx.Cast"(%1026) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[2],si64> 
    %1033 = torch.operator "onnx.Concat"(%1031, %1032) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4],si64> 
    %1034 = torch.operator "onnx.Resize"(%982, %none, %none, %1033) {torch.onnx.coordinate_transformation_mode = "half_pixel", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "linear", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[1,3,300,400],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> 
    %1035 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__770> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1036 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__771> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1037 = torch.operator "onnx.QuantizeLinear"(%1034, %1035, %1036) : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?,?,?],si8> 
    %1038 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__772> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1039 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__773> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1040 = torch.operator "onnx.DequantizeLinear"(%1037, %1038, %1039) : (!torch.vtensor<[?,?,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?,?,?],f32> 
    %1041 = torch.operator "onnx.Gather"(%1040, %284) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %1042 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__774> : tensor<3x1x1xf32>} : () -> !torch.vtensor<[3,1,1],f32> 
    %1043 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__775> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1044 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__776> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1045 = torch.operator "onnx.QuantizeLinear"(%1042, %1043, %1044) : (!torch.vtensor<[3,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],si8> 
    %1046 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__777> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1047 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__778> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1048 = torch.operator "onnx.DequantizeLinear"(%1045, %1046, %1047) : (!torch.vtensor<[3,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],f32> 
    %1049 = torch.operator "onnx.Sub"(%290, %1048) : (!torch.vtensor<[3,500,400],f32>, !torch.vtensor<[3,1,1],f32>) -> !torch.vtensor<[3,500,400],f32> 
    %1050 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__779> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1051 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__780> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1052 = torch.operator "onnx.QuantizeLinear"(%1049, %1050, %1051) : (!torch.vtensor<[3,500,400],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,500,400],si8> 
    %1053 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__781> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1054 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__782> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1055 = torch.operator "onnx.DequantizeLinear"(%1052, %1053, %1054) : (!torch.vtensor<[3,500,400],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,500,400],f32> 
    %1056 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__783> : tensor<3x1x1xf32>} : () -> !torch.vtensor<[3,1,1],f32> 
    %1057 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__784> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1058 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__785> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1059 = torch.operator "onnx.QuantizeLinear"(%1056, %1057, %1058) : (!torch.vtensor<[3,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],si8> 
    %1060 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__786> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1061 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__787> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1062 = torch.operator "onnx.DequantizeLinear"(%1059, %1060, %1061) : (!torch.vtensor<[3,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,1,1],f32> 
    %1063 = torch.operator "onnx.Div"(%1055, %1062) : (!torch.vtensor<[3,500,400],f32>, !torch.vtensor<[3,1,1],f32>) -> !torch.vtensor<[3,500,400],f32> 
    %1064 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__788> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1065 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__789> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1066 = torch.operator "onnx.QuantizeLinear"(%1063, %1064, %1065) : (!torch.vtensor<[3,500,400],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,500,400],si8> 
    %1067 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__790> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1068 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__791> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1069 = torch.operator "onnx.DequantizeLinear"(%1066, %1067, %1068) : (!torch.vtensor<[3,500,400],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,500,400],f32> 
    %1070 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__792> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %1071 = torch.operator "onnx.ReduceMin"(%1070) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %1072 = torch.operator "onnx.Cast"(%1071) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %1073 = torch.operator "onnx.ReduceMax"(%1070) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %1074 = torch.operator "onnx.Cast"(%1073) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %1075 = torch.operator "onnx.Reciprocal"(%1072) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1076 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__793> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1077 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__794> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1078 = torch.operator "onnx.QuantizeLinear"(%1075, %1076, %1077) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1079 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__795> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1080 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__796> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1081 = torch.operator "onnx.DequantizeLinear"(%1078, %1079, %1080) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1082 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__797> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1083 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__798> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1084 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__799> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1085 = torch.operator "onnx.QuantizeLinear"(%1082, %1083, %1084) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1086 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__800> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1087 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__801> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1088 = torch.operator "onnx.DequantizeLinear"(%1085, %1086, %1087) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1089 = torch.operator "onnx.Mul"(%1081, %1088) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1090 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__802> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1091 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__803> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1092 = torch.operator "onnx.QuantizeLinear"(%1089, %1090, %1091) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1093 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__804> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1094 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__805> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1095 = torch.operator "onnx.DequantizeLinear"(%1092, %1093, %1094) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1096 = torch.operator "onnx.Reciprocal"(%1074) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1097 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__806> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1098 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__807> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1099 = torch.operator "onnx.QuantizeLinear"(%1096, %1097, %1098) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1100 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__808> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1101 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__809> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1102 = torch.operator "onnx.DequantizeLinear"(%1099, %1100, %1101) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1103 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__810> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1104 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__811> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1105 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__812> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1106 = torch.operator "onnx.QuantizeLinear"(%1103, %1104, %1105) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1107 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__813> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1108 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__814> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1109 = torch.operator "onnx.DequantizeLinear"(%1106, %1107, %1108) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1110 = torch.operator "onnx.Mul"(%1102, %1109) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1111 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__815> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1112 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__816> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1113 = torch.operator "onnx.QuantizeLinear"(%1110, %1111, %1112) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1114 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__817> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1115 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__818> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1116 = torch.operator "onnx.DequantizeLinear"(%1113, %1114, %1115) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1117 = torch.operator "onnx.Min"(%1095, %1116) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1118 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__819> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1119 = torch.operator "onnx.Unsqueeze"(%1069, %1118) : (!torch.vtensor<[3,500,400],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,3,500,400],f32> 
    %1120 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__820> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1121 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__821> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1122 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__822> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1123 = torch.operator "onnx.QuantizeLinear"(%1120, %1121, %1122) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1124 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__823> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1125 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__824> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1126 = torch.operator "onnx.DequantizeLinear"(%1123, %1124, %1125) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1127 = torch.operator "onnx.Cast"(%1117) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1128 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__825> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1129 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__826> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1130 = torch.operator "onnx.QuantizeLinear"(%1127, %1128, %1129) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1131 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__827> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1132 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__828> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1133 = torch.operator "onnx.DequantizeLinear"(%1130, %1131, %1132) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1134 = torch.operator "onnx.Mul"(%1126, %1133) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1135 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__829> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1136 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__830> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1137 = torch.operator "onnx.QuantizeLinear"(%1134, %1135, %1136) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1138 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__831> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1139 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__832> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1140 = torch.operator "onnx.DequantizeLinear"(%1137, %1138, %1139) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1141 = torch.operator "onnx.Cast"(%1140) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1142 = torch.operator "onnx.Floor"(%1141) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1143 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__833> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1144 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__834> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1145 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__835> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1146 = torch.operator "onnx.QuantizeLinear"(%1143, %1144, %1145) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1147 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__836> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1148 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__837> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1149 = torch.operator "onnx.DequantizeLinear"(%1146, %1147, %1148) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1150 = torch.operator "onnx.Mul"(%1149, %1133) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1151 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__838> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1152 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__839> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1153 = torch.operator "onnx.QuantizeLinear"(%1150, %1151, %1152) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1154 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__840> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1155 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__841> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1156 = torch.operator "onnx.DequantizeLinear"(%1153, %1154, %1155) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1157 = torch.operator "onnx.Cast"(%1156) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1158 = torch.operator "onnx.Floor"(%1157) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1159 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__842> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1160 = torch.operator "onnx.Unsqueeze"(%1142, %1159) : (!torch.vtensor<[],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],f32> 
    %1161 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__843> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1162 = torch.operator "onnx.Unsqueeze"(%1158, %1161) : (!torch.vtensor<[],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],f32> 
    %1163 = torch.operator "onnx.Concat"(%1160, %1162) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],f32>, !torch.vtensor<[1],f32>) -> !torch.vtensor<[2],f32> 
    %1164 = torch.operator "onnx.Shape"(%1119) : (!torch.vtensor<[1,3,500,400],f32>) -> !torch.vtensor<[4],si64> 
    %1165 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__844> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1166 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__845> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1167 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__846> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1168 = torch.operator "onnx.Slice"(%1164, %1166, %1167, %1165) : (!torch.vtensor<[4],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %1169 = torch.operator "onnx.Cast"(%1163) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[2],si64> 
    %1170 = torch.operator "onnx.Concat"(%1168, %1169) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4],si64> 
    %1171 = torch.operator "onnx.Resize"(%1119, %none, %none, %1170) {torch.onnx.coordinate_transformation_mode = "half_pixel", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "linear", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[1,3,500,400],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> 
    %1172 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__847> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1173 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__848> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1174 = torch.operator "onnx.QuantizeLinear"(%1171, %1172, %1173) : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?,?,?],si8> 
    %1175 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__849> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1176 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__850> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1177 = torch.operator "onnx.DequantizeLinear"(%1174, %1175, %1176) : (!torch.vtensor<[?,?,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?,?,?],f32> 
    %1178 = torch.operator "onnx.Gather"(%1177, %284) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %1179 = torch.operator "onnx.Shape"(%1041) : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %1180 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__851> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1181 = torch.operator "onnx.Gather"(%1179, %1180) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1182 = torch.operator "onnx.Shape"(%1041) : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %1183 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__852> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1184 = torch.operator "onnx.Gather"(%1182, %1183) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1185 = torch.operator "onnx.Shape"(%1178) : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %1186 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__853> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1187 = torch.operator "onnx.Gather"(%1185, %1186) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1188 = torch.operator "onnx.Shape"(%1178) : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %1189 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__854> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1190 = torch.operator "onnx.Gather"(%1188, %1189) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1191 = torch.operator "onnx.Shape"(%1041) : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %1192 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__855> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1193 = torch.operator "onnx.Gather"(%1191, %1192) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1194 = torch.operator "onnx.Shape"(%1178) : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %1195 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__856> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1196 = torch.operator "onnx.Gather"(%1194, %1195) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1197 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__857> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1198 = torch.operator "onnx.Unsqueeze"(%1193, %1197) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1199 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__858> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1200 = torch.operator "onnx.Unsqueeze"(%1196, %1199) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1201 = torch.operator "onnx.Concat"(%1198, %1200) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %1202 = torch.operator "onnx.Cast"(%1201) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],f32> 
    %1203 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__859> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1204 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__860> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1205 = torch.operator "onnx.QuantizeLinear"(%1202, %1203, %1204) : (!torch.vtensor<[2],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],si8> 
    %1206 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__861> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1207 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__862> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1208 = torch.operator "onnx.DequantizeLinear"(%1205, %1206, %1207) : (!torch.vtensor<[2],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],f32> 
    %1209 = torch.operator "onnx.ReduceMax"(%1208) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[],f32> 
    %1210 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__863> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1211 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__864> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1212 = torch.operator "onnx.QuantizeLinear"(%1209, %1210, %1211) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1213 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__865> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1214 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__866> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1215 = torch.operator "onnx.DequantizeLinear"(%1212, %1213, %1214) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1216 = torch.operator "onnx.Cast"(%1215) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %1217 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__867> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1218 = torch.operator "onnx.Unsqueeze"(%1181, %1217) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1219 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__868> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1220 = torch.operator "onnx.Unsqueeze"(%1187, %1219) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1221 = torch.operator "onnx.Concat"(%1218, %1220) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %1222 = torch.operator "onnx.Cast"(%1221) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],f32> 
    %1223 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__869> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1224 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__870> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1225 = torch.operator "onnx.QuantizeLinear"(%1222, %1223, %1224) : (!torch.vtensor<[2],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],si8> 
    %1226 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__871> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1227 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__872> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1228 = torch.operator "onnx.DequantizeLinear"(%1225, %1226, %1227) : (!torch.vtensor<[2],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],f32> 
    %1229 = torch.operator "onnx.ReduceMax"(%1228) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[],f32> 
    %1230 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__873> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1231 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__874> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1232 = torch.operator "onnx.QuantizeLinear"(%1229, %1230, %1231) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1233 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__875> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1234 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__876> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1235 = torch.operator "onnx.DequantizeLinear"(%1232, %1233, %1234) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1236 = torch.operator "onnx.Cast"(%1235) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %1237 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__877> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1238 = torch.operator "onnx.Unsqueeze"(%1184, %1237) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1239 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__878> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1240 = torch.operator "onnx.Unsqueeze"(%1190, %1239) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1241 = torch.operator "onnx.Concat"(%1238, %1240) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %1242 = torch.operator "onnx.Cast"(%1241) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],f32> 
    %1243 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__879> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1244 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__880> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1245 = torch.operator "onnx.QuantizeLinear"(%1242, %1243, %1244) : (!torch.vtensor<[2],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],si8> 
    %1246 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__881> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1247 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__882> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1248 = torch.operator "onnx.DequantizeLinear"(%1245, %1246, %1247) : (!torch.vtensor<[2],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2],f32> 
    %1249 = torch.operator "onnx.ReduceMax"(%1248) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],f32>) -> !torch.vtensor<[],f32> 
    %1250 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__883> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1251 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__884> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1252 = torch.operator "onnx.QuantizeLinear"(%1249, %1250, %1251) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1253 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__885> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1254 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__886> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1255 = torch.operator "onnx.DequantizeLinear"(%1252, %1253, %1254) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1256 = torch.operator "onnx.Cast"(%1255) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %1257 = torch.operator "onnx.Cast"(%1236) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %1258 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__887> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1259 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__888> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1260 = torch.operator "onnx.QuantizeLinear"(%1257, %1258, %1259) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1261 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__889> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1262 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__890> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1263 = torch.operator "onnx.DequantizeLinear"(%1260, %1261, %1262) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1264 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__891> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1265 = torch.operator "onnx.Div"(%1263, %1264) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1266 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__892> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1267 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__893> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1268 = torch.operator "onnx.QuantizeLinear"(%1265, %1266, %1267) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1269 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__894> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1270 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__895> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1271 = torch.operator "onnx.DequantizeLinear"(%1268, %1269, %1270) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1272 = torch.operator "onnx.Ceil"(%1271) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1273 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__896> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1274 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__897> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1275 = torch.operator "onnx.QuantizeLinear"(%1272, %1273, %1274) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1276 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__898> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1277 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__899> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1278 = torch.operator "onnx.DequantizeLinear"(%1275, %1276, %1277) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1279 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__900> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1280 = torch.operator "onnx.Mul"(%1278, %1279) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1281 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__901> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1282 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__902> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1283 = torch.operator "onnx.QuantizeLinear"(%1280, %1281, %1282) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1284 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__903> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1285 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__904> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1286 = torch.operator "onnx.DequantizeLinear"(%1283, %1284, %1285) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1287 = torch.operator "onnx.Cast"(%1286) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %1288 = torch.operator "onnx.Cast"(%1256) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %1289 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__905> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1290 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__906> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1291 = torch.operator "onnx.QuantizeLinear"(%1288, %1289, %1290) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1292 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__907> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1293 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__908> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1294 = torch.operator "onnx.DequantizeLinear"(%1291, %1292, %1293) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1295 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__909> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1296 = torch.operator "onnx.Div"(%1294, %1295) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1297 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__910> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1298 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__911> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1299 = torch.operator "onnx.QuantizeLinear"(%1296, %1297, %1298) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1300 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__912> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1301 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__913> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1302 = torch.operator "onnx.DequantizeLinear"(%1299, %1300, %1301) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1303 = torch.operator "onnx.Ceil"(%1302) : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1304 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__914> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1305 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__915> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1306 = torch.operator "onnx.QuantizeLinear"(%1303, %1304, %1305) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1307 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__916> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1308 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__917> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1309 = torch.operator "onnx.DequantizeLinear"(%1306, %1307, %1308) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1310 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__918> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1311 = torch.operator "onnx.Mul"(%1309, %1310) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
    %1312 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__919> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1313 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__920> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1314 = torch.operator "onnx.QuantizeLinear"(%1311, %1312, %1313) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %1315 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__921> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1316 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__922> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1317 = torch.operator "onnx.DequantizeLinear"(%1314, %1315, %1316) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %1318 = torch.operator "onnx.Cast"(%1317) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %1319 = torch.operator "onnx.Sub"(%1216, %1193) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1320 = torch.operator "onnx.Sub"(%1287, %1181) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1321 = torch.operator "onnx.Sub"(%1318, %1184) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1322 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__923> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1323 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__924> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1324 = torch.operator "onnx.Unsqueeze"(%1321, %1323) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1325 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__925> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1326 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__926> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1327 = torch.operator "onnx.Unsqueeze"(%1320, %1326) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1328 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__927> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1329 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__928> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1330 = torch.operator "onnx.Unsqueeze"(%1319, %1329) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1331 = torch.operator "onnx.Concat"(%1322, %1324, %1325, %1327, %1328, %1330) {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> 
    %1332 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__929> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1333 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__930> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1334 = torch.operator "onnx.Unsqueeze"(%1321, %1333) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1335 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__931> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1336 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__932> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1337 = torch.operator "onnx.Unsqueeze"(%1320, %1336) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1338 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__933> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1339 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__934> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1340 = torch.operator "onnx.Unsqueeze"(%1319, %1339) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1341 = torch.operator "onnx.Concat"(%1332, %1334, %1335, %1337, %1338, %1340) {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> 
    %1342 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__935> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1343 = torch.operator "onnx.Shape"(%1331) : (!torch.vtensor<[6],si64>) -> !torch.vtensor<[1],si64> 
    %1344 = torch.operator "onnx.Gather"(%1343, %1342) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1345 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__936> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1346 = torch.operator "onnx.Sub"(%1345, %1344) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1347 = torch.operator "onnx.Cast"(%1341) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64> 
    %1348 = torch.operator "onnx.ConstantOfShape"(%1346) {torch.onnx.value = dense_resource<__937> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[0],si64> 
    %1349 = torch.operator "onnx.Concat"(%1347, %1348) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[6],si64>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[6],si64> 
    %1350 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__938> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %1351 = torch.operator "onnx.Reshape"(%1349, %1350) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[6],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[3,2],si64> 
    %1352 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__939> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1353 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__940> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1354 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__941> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1355 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__942> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1356 = torch.operator "onnx.Slice"(%1351, %1353, %1354, %1352, %1355) : (!torch.vtensor<[3,2],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],si64> 
    %1357 = torch.operator "onnx.Transpose"(%1356) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[3,2],si64>) -> !torch.vtensor<[2,3],si64> 
    %1358 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__943> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1359 = torch.operator "onnx.Reshape"(%1357, %1358) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,3],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[6],si64> 
    %1360 = torch.operator "onnx.Cast"(%1359) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64> 
    %1361 = torch.operator "onnx.Pad"(%1041, %1360, %none) {torch.onnx.mode = "constant"} : (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[6],si64>, !torch.none) -> !torch.vtensor<[?,?,?],f32> 
    %1362 = torch.operator "onnx.Sub"(%1216, %1196) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1363 = torch.operator "onnx.Sub"(%1287, %1187) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1364 = torch.operator "onnx.Sub"(%1318, %1190) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %1365 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__944> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1366 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__945> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1367 = torch.operator "onnx.Unsqueeze"(%1364, %1366) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1368 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__946> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1369 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__947> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1370 = torch.operator "onnx.Unsqueeze"(%1363, %1369) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1371 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__948> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1372 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__949> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1373 = torch.operator "onnx.Unsqueeze"(%1362, %1372) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1374 = torch.operator "onnx.Concat"(%1365, %1367, %1368, %1370, %1371, %1373) {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> 
    %1375 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__950> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1376 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__951> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1377 = torch.operator "onnx.Unsqueeze"(%1364, %1376) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1378 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__952> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1379 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__953> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1380 = torch.operator "onnx.Unsqueeze"(%1363, %1379) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1381 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__954> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1382 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__955> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1383 = torch.operator "onnx.Unsqueeze"(%1362, %1382) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1384 = torch.operator "onnx.Concat"(%1375, %1377, %1378, %1380, %1381, %1383) {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> 
    %1385 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__956> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1386 = torch.operator "onnx.Shape"(%1374) : (!torch.vtensor<[6],si64>) -> !torch.vtensor<[1],si64> 
    %1387 = torch.operator "onnx.Gather"(%1386, %1385) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1388 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__957> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %1389 = torch.operator "onnx.Sub"(%1388, %1387) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %1390 = torch.operator "onnx.Cast"(%1384) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64> 
    %1391 = torch.operator "onnx.ConstantOfShape"(%1389) {torch.onnx.value = dense_resource<__958> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[0],si64> 
    %1392 = torch.operator "onnx.Concat"(%1390, %1391) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[6],si64>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[6],si64> 
    %1393 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__959> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %1394 = torch.operator "onnx.Reshape"(%1392, %1393) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[6],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[3,2],si64> 
    %1395 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__960> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1396 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__961> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1397 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__962> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1398 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__963> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1399 = torch.operator "onnx.Slice"(%1394, %1396, %1397, %1395, %1398) : (!torch.vtensor<[3,2],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],si64> 
    %1400 = torch.operator "onnx.Transpose"(%1399) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[3,2],si64>) -> !torch.vtensor<[2,3],si64> 
    %1401 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__964> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1402 = torch.operator "onnx.Reshape"(%1400, %1401) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,3],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[6],si64> 
    %1403 = torch.operator "onnx.Cast"(%1402) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[6],si64>) -> !torch.vtensor<[6],si64> 
    %1404 = torch.operator "onnx.Pad"(%1178, %1403, %none) {torch.onnx.mode = "constant"} : (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[6],si64>, !torch.none) -> !torch.vtensor<[?,?,?],f32> 
    %1405 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__965> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1406 = torch.operator "onnx.Unsqueeze"(%1361, %1405) : (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,?,?,?],f32> 
    %1407 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__966> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %1408 = torch.operator "onnx.Unsqueeze"(%1404, %1407) : (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,?,?,?],f32> 
    %1409 = torch.operator "onnx.Concat"(%1406, %1408) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1,?,?,?],f32>, !torch.vtensor<[1,?,?,?],f32>) -> !torch.vtensor<[2,?,?,?],f32> 
    %1410 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__967> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1411 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__968> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1412 = torch.operator "onnx.QuantizeLinear"(%1409, %1410, %1411) : (!torch.vtensor<[2,?,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,?,?,?],si8> 
    %1413 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__969> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1414 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__970> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1415 = torch.operator "onnx.DequantizeLinear"(%1412, %1413, %1414) : (!torch.vtensor<[2,?,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,?,?,?],f32> 
    %1416 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__971> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1417 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__972> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1418 = torch.operator "onnx.QuantizeLinear"(%171, %1416, %1417) : (!torch.vtensor<[64],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],si8> 
    %1419 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__973> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1420 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__974> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1421 = torch.operator "onnx.DequantizeLinear"(%1418, %1419, %1420) : (!torch.vtensor<[64],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],f32> 
    %1422 = torch.operator "onnx.Conv"(%1415, %908, %1421) {torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [7 : si64, 7 : si64], torch.onnx.pads = [3 : si64, 3 : si64, 3 : si64, 3 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,?,?,?],f32>, !torch.vtensor<[64,3,7,7],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1423 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__975> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1424 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__976> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1425 = torch.operator "onnx.QuantizeLinear"(%1422, %1423, %1424) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1426 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__977> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1427 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__978> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1428 = torch.operator "onnx.DequantizeLinear"(%1425, %1426, %1427) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1429 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__979> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1430 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__980> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1431 = torch.operator "onnx.QuantizeLinear"(%170, %1429, %1430) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1432 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__981> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1433 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__982> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1434 = torch.operator "onnx.DequantizeLinear"(%1431, %1432, %1433) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1435 = torch.operator "onnx.Mul"(%1428, %1434) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1436 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__983> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1437 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__984> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1438 = torch.operator "onnx.QuantizeLinear"(%1435, %1436, %1437) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1439 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__985> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1440 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__986> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1441 = torch.operator "onnx.DequantizeLinear"(%1438, %1439, %1440) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1442 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__987> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1443 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__988> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1444 = torch.operator "onnx.QuantizeLinear"(%169, %1442, %1443) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1445 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__989> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1446 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__990> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1447 = torch.operator "onnx.DequantizeLinear"(%1444, %1445, %1446) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1448 = torch.operator "onnx.Add"(%1441, %1447) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1449 = torch.operator "onnx.Relu"(%1448) : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1450 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__991> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1451 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__992> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1452 = torch.operator "onnx.QuantizeLinear"(%1449, %1450, %1451) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1453 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__993> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1454 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__994> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1455 = torch.operator "onnx.DequantizeLinear"(%1452, %1453, %1454) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1456 = torch.operator "onnx.MaxPool"(%1455) {torch.onnx.ceil_mode = 0 : si64, torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [1 : si64, 1 : si64, 1 : si64, 1 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1457 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__995> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1458 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__996> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1459 = torch.operator "onnx.QuantizeLinear"(%1456, %1457, %1458) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1460 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__997> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1461 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__998> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1462 = torch.operator "onnx.DequantizeLinear"(%1459, %1460, %1461) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1463 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__999> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1464 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1000> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1465 = torch.operator "onnx.QuantizeLinear"(%168, %1463, %1464) : (!torch.vtensor<[64],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],si8> 
    %1466 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1001> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1467 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1002> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1468 = torch.operator "onnx.DequantizeLinear"(%1465, %1466, %1467) : (!torch.vtensor<[64],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],f32> 
    %1469 = torch.operator "onnx.Conv"(%1462, %902, %1468) {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<[2,64,?,?],f32>, !torch.vtensor<[64,64,1,1],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1470 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1003> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1471 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1004> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1472 = torch.operator "onnx.QuantizeLinear"(%1469, %1470, %1471) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1473 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1005> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1474 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1006> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1475 = torch.operator "onnx.DequantizeLinear"(%1472, %1473, %1474) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1476 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1007> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1477 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1008> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1478 = torch.operator "onnx.QuantizeLinear"(%167, %1476, %1477) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1479 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1009> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1480 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1010> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1481 = torch.operator "onnx.DequantizeLinear"(%1478, %1479, %1480) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1482 = torch.operator "onnx.Mul"(%1475, %1481) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1483 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1011> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1484 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1012> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1485 = torch.operator "onnx.QuantizeLinear"(%1482, %1483, %1484) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1486 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1013> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1487 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1014> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1488 = torch.operator "onnx.DequantizeLinear"(%1485, %1486, %1487) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1489 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1015> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1490 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1016> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1491 = torch.operator "onnx.QuantizeLinear"(%166, %1489, %1490) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1492 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1017> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1493 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1018> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1494 = torch.operator "onnx.DequantizeLinear"(%1491, %1492, %1493) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1495 = torch.operator "onnx.Add"(%1488, %1494) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1496 = torch.operator "onnx.Relu"(%1495) : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1497 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1019> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1498 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1020> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1499 = torch.operator "onnx.QuantizeLinear"(%1496, %1497, %1498) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1500 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1021> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1501 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1022> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1502 = torch.operator "onnx.DequantizeLinear"(%1499, %1500, %1501) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1503 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1023> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1504 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1024> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1505 = torch.operator "onnx.QuantizeLinear"(%165, %1503, %1504) : (!torch.vtensor<[64],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],si8> 
    %1506 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1025> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1507 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1026> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1508 = torch.operator "onnx.DequantizeLinear"(%1505, %1506, %1507) : (!torch.vtensor<[64],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],f32> 
    %1509 = torch.operator "onnx.Conv"(%1502, %896, %1508) {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<[2,64,?,?],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1510 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1027> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1511 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1028> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1512 = torch.operator "onnx.QuantizeLinear"(%1509, %1510, %1511) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1513 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1029> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1514 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1030> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1515 = torch.operator "onnx.DequantizeLinear"(%1512, %1513, %1514) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1516 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1031> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1517 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1032> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1518 = torch.operator "onnx.QuantizeLinear"(%164, %1516, %1517) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1519 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1033> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1520 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1034> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1521 = torch.operator "onnx.DequantizeLinear"(%1518, %1519, %1520) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1522 = torch.operator "onnx.Mul"(%1515, %1521) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1523 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1035> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1524 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1036> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1525 = torch.operator "onnx.QuantizeLinear"(%1522, %1523, %1524) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1526 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1037> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1527 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1038> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1528 = torch.operator "onnx.DequantizeLinear"(%1525, %1526, %1527) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1529 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1039> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1530 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1040> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1531 = torch.operator "onnx.QuantizeLinear"(%163, %1529, %1530) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1532 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1041> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1533 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1042> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1534 = torch.operator "onnx.DequantizeLinear"(%1531, %1532, %1533) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1535 = torch.operator "onnx.Add"(%1528, %1534) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1536 = torch.operator "onnx.Relu"(%1535) : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1537 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1043> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1538 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1044> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1539 = torch.operator "onnx.QuantizeLinear"(%1536, %1537, %1538) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1540 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1045> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1541 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1046> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1542 = torch.operator "onnx.DequantizeLinear"(%1539, %1540, %1541) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1543 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1047> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1544 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1048> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1545 = torch.operator "onnx.QuantizeLinear"(%162, %1543, %1544) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %1546 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1049> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1547 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1050> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1548 = torch.operator "onnx.DequantizeLinear"(%1545, %1546, %1547) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %1549 = torch.operator "onnx.Conv"(%1542, %890, %1548) {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<[2,64,?,?],f32>, !torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1550 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1051> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1551 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1052> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1552 = torch.operator "onnx.QuantizeLinear"(%1549, %1550, %1551) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1553 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1053> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1554 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1054> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1555 = torch.operator "onnx.DequantizeLinear"(%1552, %1553, %1554) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1556 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1055> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1557 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1056> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1558 = torch.operator "onnx.QuantizeLinear"(%161, %1556, %1557) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1559 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1057> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1560 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1058> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1561 = torch.operator "onnx.DequantizeLinear"(%1558, %1559, %1560) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1562 = torch.operator "onnx.Mul"(%1555, %1561) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1563 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1059> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1564 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1060> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1565 = torch.operator "onnx.QuantizeLinear"(%1562, %1563, %1564) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1566 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1061> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1567 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1062> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1568 = torch.operator "onnx.DequantizeLinear"(%1565, %1566, %1567) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1569 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1063> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1570 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1064> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1571 = torch.operator "onnx.QuantizeLinear"(%160, %1569, %1570) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1572 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1065> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1573 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1066> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1574 = torch.operator "onnx.DequantizeLinear"(%1571, %1572, %1573) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1575 = torch.operator "onnx.Add"(%1568, %1574) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1576 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1067> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1577 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1068> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1578 = torch.operator "onnx.QuantizeLinear"(%1575, %1576, %1577) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1579 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1069> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1580 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1070> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1581 = torch.operator "onnx.DequantizeLinear"(%1578, %1579, %1580) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1582 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1071> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1583 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1072> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1584 = torch.operator "onnx.QuantizeLinear"(%159, %1582, %1583) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %1585 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1073> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1586 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1074> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1587 = torch.operator "onnx.DequantizeLinear"(%1584, %1585, %1586) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %1588 = torch.operator "onnx.Conv"(%1462, %884, %1587) {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<[2,64,?,?],f32>, !torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1589 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1075> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1590 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1076> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1591 = torch.operator "onnx.QuantizeLinear"(%1588, %1589, %1590) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1592 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1077> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1593 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1078> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1594 = torch.operator "onnx.DequantizeLinear"(%1591, %1592, %1593) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1595 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1079> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1596 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1080> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1597 = torch.operator "onnx.QuantizeLinear"(%158, %1595, %1596) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1598 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1081> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1599 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1082> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1600 = torch.operator "onnx.DequantizeLinear"(%1597, %1598, %1599) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1601 = torch.operator "onnx.Mul"(%1594, %1600) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1602 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1083> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1603 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1084> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1604 = torch.operator "onnx.QuantizeLinear"(%1601, %1602, %1603) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1605 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1085> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1606 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1086> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1607 = torch.operator "onnx.DequantizeLinear"(%1604, %1605, %1606) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1608 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1087> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1609 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1088> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1610 = torch.operator "onnx.QuantizeLinear"(%157, %1608, %1609) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1611 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1089> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1612 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1090> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1613 = torch.operator "onnx.DequantizeLinear"(%1610, %1611, %1612) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1614 = torch.operator "onnx.Add"(%1607, %1613) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1615 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1091> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1616 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1092> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1617 = torch.operator "onnx.QuantizeLinear"(%1614, %1615, %1616) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1618 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1093> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1619 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1094> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1620 = torch.operator "onnx.DequantizeLinear"(%1617, %1618, %1619) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1621 = torch.operator "onnx.Add"(%1581, %1620) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1622 = torch.operator "onnx.Relu"(%1621) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1623 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1095> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1624 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1096> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1625 = torch.operator "onnx.QuantizeLinear"(%1622, %1623, %1624) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1626 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1097> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1098> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1628 = torch.operator "onnx.DequantizeLinear"(%1625, %1626, %1627) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1629 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1099> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1630 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1100> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1631 = torch.operator "onnx.QuantizeLinear"(%156, %1629, %1630) : (!torch.vtensor<[64],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],si8> 
    %1632 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1101> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1633 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1102> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1634 = torch.operator "onnx.DequantizeLinear"(%1631, %1632, %1633) : (!torch.vtensor<[64],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],f32> 
    %1635 = torch.operator "onnx.Conv"(%1628, %878, %1634) {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<[2,256,?,?],f32>, !torch.vtensor<[64,256,1,1],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1636 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1103> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1637 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1104> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1638 = torch.operator "onnx.QuantizeLinear"(%1635, %1636, %1637) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1639 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1105> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1640 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1106> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1641 = torch.operator "onnx.DequantizeLinear"(%1638, %1639, %1640) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1642 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1107> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1643 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1108> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1644 = torch.operator "onnx.QuantizeLinear"(%155, %1642, %1643) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1645 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1109> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1646 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1110> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1647 = torch.operator "onnx.DequantizeLinear"(%1644, %1645, %1646) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1648 = torch.operator "onnx.Mul"(%1641, %1647) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1649 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1111> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1650 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1112> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1651 = torch.operator "onnx.QuantizeLinear"(%1648, %1649, %1650) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1113> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1653 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1114> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1654 = torch.operator "onnx.DequantizeLinear"(%1651, %1652, %1653) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1655 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1115> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1656 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1116> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1657 = torch.operator "onnx.QuantizeLinear"(%154, %1655, %1656) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1658 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1117> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1659 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1118> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1660 = torch.operator "onnx.DequantizeLinear"(%1657, %1658, %1659) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1661 = torch.operator "onnx.Add"(%1654, %1660) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1662 = torch.operator "onnx.Relu"(%1661) : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1663 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1119> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1664 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1120> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1665 = torch.operator "onnx.QuantizeLinear"(%1662, %1663, %1664) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1121> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1667 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1122> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1668 = torch.operator "onnx.DequantizeLinear"(%1665, %1666, %1667) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1669 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1123> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1670 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1124> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1671 = torch.operator "onnx.QuantizeLinear"(%153, %1669, %1670) : (!torch.vtensor<[64],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],si8> 
    %1672 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1125> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1673 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1126> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1674 = torch.operator "onnx.DequantizeLinear"(%1671, %1672, %1673) : (!torch.vtensor<[64],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],f32> 
    %1675 = torch.operator "onnx.Conv"(%1668, %872, %1674) {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<[2,64,?,?],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1676 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1127> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1677 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1128> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1678 = torch.operator "onnx.QuantizeLinear"(%1675, %1676, %1677) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1679 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1129> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1680 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1130> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1681 = torch.operator "onnx.DequantizeLinear"(%1678, %1679, %1680) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1682 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1131> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1683 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1132> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1684 = torch.operator "onnx.QuantizeLinear"(%152, %1682, %1683) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1685 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1133> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1686 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1134> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1687 = torch.operator "onnx.DequantizeLinear"(%1684, %1685, %1686) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1688 = torch.operator "onnx.Mul"(%1681, %1687) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1689 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1135> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1690 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1136> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1691 = torch.operator "onnx.QuantizeLinear"(%1688, %1689, %1690) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1692 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1137> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1693 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1138> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1694 = torch.operator "onnx.DequantizeLinear"(%1691, %1692, %1693) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1695 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1139> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1696 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1140> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1697 = torch.operator "onnx.QuantizeLinear"(%151, %1695, %1696) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1698 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1141> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1699 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1142> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1700 = torch.operator "onnx.DequantizeLinear"(%1697, %1698, %1699) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1701 = torch.operator "onnx.Add"(%1694, %1700) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1702 = torch.operator "onnx.Relu"(%1701) : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1703 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1143> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1704 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1144> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1705 = torch.operator "onnx.QuantizeLinear"(%1702, %1703, %1704) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1706 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1145> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1707 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1146> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1708 = torch.operator "onnx.DequantizeLinear"(%1705, %1706, %1707) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1709 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1147> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1710 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1148> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1711 = torch.operator "onnx.QuantizeLinear"(%150, %1709, %1710) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %1712 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1149> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1713 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1150> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1714 = torch.operator "onnx.DequantizeLinear"(%1711, %1712, %1713) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %1715 = torch.operator "onnx.Conv"(%1708, %866, %1714) {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<[2,64,?,?],f32>, !torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1716 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1151> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1717 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1152> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1718 = torch.operator "onnx.QuantizeLinear"(%1715, %1716, %1717) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1719 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1153> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1720 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1154> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1721 = torch.operator "onnx.DequantizeLinear"(%1718, %1719, %1720) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1722 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1155> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1723 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1156> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1724 = torch.operator "onnx.QuantizeLinear"(%149, %1722, %1723) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1725 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1157> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1726 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1158> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1727 = torch.operator "onnx.DequantizeLinear"(%1724, %1725, %1726) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1728 = torch.operator "onnx.Mul"(%1721, %1727) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1729 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1159> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1730 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1160> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1731 = torch.operator "onnx.QuantizeLinear"(%1728, %1729, %1730) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1732 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1161> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1733 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1162> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1734 = torch.operator "onnx.DequantizeLinear"(%1731, %1732, %1733) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1735 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1163> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1736 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1164> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1737 = torch.operator "onnx.QuantizeLinear"(%148, %1735, %1736) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1738 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1165> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1739 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1166> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1740 = torch.operator "onnx.DequantizeLinear"(%1737, %1738, %1739) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1741 = torch.operator "onnx.Add"(%1734, %1740) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1742 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1167> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1743 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1168> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1744 = torch.operator "onnx.QuantizeLinear"(%1741, %1742, %1743) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1745 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1169> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1746 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1170> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1747 = torch.operator "onnx.DequantizeLinear"(%1744, %1745, %1746) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1748 = torch.operator "onnx.Add"(%1747, %1628) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1749 = torch.operator "onnx.Relu"(%1748) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1750 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1171> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1751 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1172> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1752 = torch.operator "onnx.QuantizeLinear"(%1749, %1750, %1751) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1753 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1173> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1754 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1174> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1755 = torch.operator "onnx.DequantizeLinear"(%1752, %1753, %1754) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1756 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1175> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1757 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1176> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1758 = torch.operator "onnx.QuantizeLinear"(%147, %1756, %1757) : (!torch.vtensor<[64],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],si8> 
    %1759 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1177> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1760 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1178> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1761 = torch.operator "onnx.DequantizeLinear"(%1758, %1759, %1760) : (!torch.vtensor<[64],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],f32> 
    %1762 = torch.operator "onnx.Conv"(%1755, %860, %1761) {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<[2,256,?,?],f32>, !torch.vtensor<[64,256,1,1],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1763 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1179> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1764 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1180> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1765 = torch.operator "onnx.QuantizeLinear"(%1762, %1763, %1764) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1766 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1181> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1767 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1182> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1768 = torch.operator "onnx.DequantizeLinear"(%1765, %1766, %1767) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1769 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1183> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1770 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1184> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1771 = torch.operator "onnx.QuantizeLinear"(%146, %1769, %1770) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1772 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1185> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1773 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1186> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1774 = torch.operator "onnx.DequantizeLinear"(%1771, %1772, %1773) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1775 = torch.operator "onnx.Mul"(%1768, %1774) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1776 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1187> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1777 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1188> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1778 = torch.operator "onnx.QuantizeLinear"(%1775, %1776, %1777) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1779 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1189> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1780 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1190> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1781 = torch.operator "onnx.DequantizeLinear"(%1778, %1779, %1780) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1782 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1191> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1783 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1192> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1784 = torch.operator "onnx.QuantizeLinear"(%145, %1782, %1783) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1785 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1193> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1786 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1194> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1787 = torch.operator "onnx.DequantizeLinear"(%1784, %1785, %1786) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1788 = torch.operator "onnx.Add"(%1781, %1787) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1789 = torch.operator "onnx.Relu"(%1788) : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1790 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1195> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1791 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1196> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1792 = torch.operator "onnx.QuantizeLinear"(%1789, %1790, %1791) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1793 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1197> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1794 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1198> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1795 = torch.operator "onnx.DequantizeLinear"(%1792, %1793, %1794) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1796 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1199> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1797 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1200> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1798 = torch.operator "onnx.QuantizeLinear"(%144, %1796, %1797) : (!torch.vtensor<[64],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],si8> 
    %1799 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1201> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1800 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1202> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1801 = torch.operator "onnx.DequantizeLinear"(%1798, %1799, %1800) : (!torch.vtensor<[64],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[64],f32> 
    %1802 = torch.operator "onnx.Conv"(%1795, %854, %1801) {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<[2,64,?,?],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1803 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1203> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1804 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1204> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1805 = torch.operator "onnx.QuantizeLinear"(%1802, %1803, %1804) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1806 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1205> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1807 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1206> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1808 = torch.operator "onnx.DequantizeLinear"(%1805, %1806, %1807) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1809 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1207> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1810 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1208> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1811 = torch.operator "onnx.QuantizeLinear"(%143, %1809, %1810) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1812 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1209> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1813 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1210> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1814 = torch.operator "onnx.DequantizeLinear"(%1811, %1812, %1813) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1815 = torch.operator "onnx.Mul"(%1808, %1814) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1816 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1211> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1817 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1212> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1818 = torch.operator "onnx.QuantizeLinear"(%1815, %1816, %1817) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1819 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1213> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1820 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1214> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1821 = torch.operator "onnx.DequantizeLinear"(%1818, %1819, %1820) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1822 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1215> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1823 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1216> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1824 = torch.operator "onnx.QuantizeLinear"(%142, %1822, %1823) : (!torch.vtensor<[1,64,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],si8> 
    %1825 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1217> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1826 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1218> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1827 = torch.operator "onnx.DequantizeLinear"(%1824, %1825, %1826) : (!torch.vtensor<[1,64,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,64,1,1],f32> 
    %1828 = torch.operator "onnx.Add"(%1821, %1827) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[1,64,1,1],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1829 = torch.operator "onnx.Relu"(%1828) : (!torch.vtensor<[2,64,?,?],f32>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1830 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1219> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1831 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1220> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1832 = torch.operator "onnx.QuantizeLinear"(%1829, %1830, %1831) : (!torch.vtensor<[2,64,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],si8> 
    %1833 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1221> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1834 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1222> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1835 = torch.operator "onnx.DequantizeLinear"(%1832, %1833, %1834) : (!torch.vtensor<[2,64,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,64,?,?],f32> 
    %1836 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1223> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1837 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1224> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1838 = torch.operator "onnx.QuantizeLinear"(%141, %1836, %1837) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %1839 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1225> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1840 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1226> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1841 = torch.operator "onnx.DequantizeLinear"(%1838, %1839, %1840) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %1842 = torch.operator "onnx.Conv"(%1835, %848, %1841) {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<[2,64,?,?],f32>, !torch.vtensor<[256,64,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1843 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1227> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1844 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1228> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1845 = torch.operator "onnx.QuantizeLinear"(%1842, %1843, %1844) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1846 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1229> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1847 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1230> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1848 = torch.operator "onnx.DequantizeLinear"(%1845, %1846, %1847) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1849 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1231> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1850 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1232> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1851 = torch.operator "onnx.QuantizeLinear"(%140, %1849, %1850) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1852 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1233> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1853 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1234> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1854 = torch.operator "onnx.DequantizeLinear"(%1851, %1852, %1853) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1855 = torch.operator "onnx.Mul"(%1848, %1854) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1856 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1235> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1857 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1236> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1858 = torch.operator "onnx.QuantizeLinear"(%1855, %1856, %1857) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1859 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1237> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1860 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1238> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1861 = torch.operator "onnx.DequantizeLinear"(%1858, %1859, %1860) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1862 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1239> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1863 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1240> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1864 = torch.operator "onnx.QuantizeLinear"(%139, %1862, %1863) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %1865 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1241> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1866 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1242> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1867 = torch.operator "onnx.DequantizeLinear"(%1864, %1865, %1866) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %1868 = torch.operator "onnx.Add"(%1861, %1867) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1869 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1243> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1870 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1244> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1871 = torch.operator "onnx.QuantizeLinear"(%1868, %1869, %1870) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1872 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1245> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1873 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1246> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1874 = torch.operator "onnx.DequantizeLinear"(%1871, %1872, %1873) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1875 = torch.operator "onnx.Add"(%1874, %1755) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1876 = torch.operator "onnx.Relu"(%1875) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1877 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1247> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1878 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1248> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1879 = torch.operator "onnx.QuantizeLinear"(%1876, %1877, %1878) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %1880 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1249> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1881 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1250> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1882 = torch.operator "onnx.DequantizeLinear"(%1879, %1880, %1881) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %1883 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1251> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1884 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1252> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1885 = torch.operator "onnx.QuantizeLinear"(%138, %1883, %1884) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %1886 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1253> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1887 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1254> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1888 = torch.operator "onnx.DequantizeLinear"(%1885, %1886, %1887) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %1889 = torch.operator "onnx.Conv"(%1882, %842, %1888) {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<[2,256,?,?],f32>, !torch.vtensor<[128,256,1,1],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1890 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1255> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1891 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1256> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1892 = torch.operator "onnx.QuantizeLinear"(%1889, %1890, %1891) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %1893 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1257> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1894 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1258> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1895 = torch.operator "onnx.DequantizeLinear"(%1892, %1893, %1894) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1896 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1259> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1897 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1260> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1898 = torch.operator "onnx.QuantizeLinear"(%137, %1896, %1897) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %1899 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1261> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1900 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1262> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1901 = torch.operator "onnx.DequantizeLinear"(%1898, %1899, %1900) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %1902 = torch.operator "onnx.Mul"(%1895, %1901) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1903 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1263> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1904 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1264> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1905 = torch.operator "onnx.QuantizeLinear"(%1902, %1903, %1904) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %1906 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1265> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1907 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1266> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1908 = torch.operator "onnx.DequantizeLinear"(%1905, %1906, %1907) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1909 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1267> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1910 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1268> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1911 = torch.operator "onnx.QuantizeLinear"(%136, %1909, %1910) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %1912 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1269> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1913 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1270> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1914 = torch.operator "onnx.DequantizeLinear"(%1911, %1912, %1913) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %1915 = torch.operator "onnx.Add"(%1908, %1914) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1916 = torch.operator "onnx.Relu"(%1915) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1917 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1271> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1918 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1272> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1919 = torch.operator "onnx.QuantizeLinear"(%1916, %1917, %1918) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %1920 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1273> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1921 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1274> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1922 = torch.operator "onnx.DequantizeLinear"(%1919, %1920, %1921) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1923 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1275> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1924 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1276> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1925 = torch.operator "onnx.QuantizeLinear"(%135, %1923, %1924) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %1926 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1277> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1927 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1278> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1928 = torch.operator "onnx.DequantizeLinear"(%1925, %1926, %1927) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %1929 = torch.operator "onnx.Conv"(%1922, %836, %1928) {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 = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1930 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1279> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1931 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1280> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1932 = torch.operator "onnx.QuantizeLinear"(%1929, %1930, %1931) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %1933 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1281> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1934 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1282> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1935 = torch.operator "onnx.DequantizeLinear"(%1932, %1933, %1934) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1936 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1283> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1937 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1284> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1938 = torch.operator "onnx.QuantizeLinear"(%134, %1936, %1937) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %1939 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1285> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1940 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1286> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1941 = torch.operator "onnx.DequantizeLinear"(%1938, %1939, %1940) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %1942 = torch.operator "onnx.Mul"(%1935, %1941) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1943 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1287> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1944 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1288> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1945 = torch.operator "onnx.QuantizeLinear"(%1942, %1943, %1944) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %1946 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1289> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1947 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1290> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1948 = torch.operator "onnx.DequantizeLinear"(%1945, %1946, %1947) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1949 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1291> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1950 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1292> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1951 = torch.operator "onnx.QuantizeLinear"(%133, %1949, %1950) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %1952 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1293> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1953 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1294> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1954 = torch.operator "onnx.DequantizeLinear"(%1951, %1952, %1953) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %1955 = torch.operator "onnx.Add"(%1948, %1954) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1956 = torch.operator "onnx.Relu"(%1955) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1957 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1295> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1958 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1296> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1959 = torch.operator "onnx.QuantizeLinear"(%1956, %1957, %1958) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %1960 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1297> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1961 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1298> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1962 = torch.operator "onnx.DequantizeLinear"(%1959, %1960, %1961) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %1963 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1299> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1964 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1300> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1965 = torch.operator "onnx.QuantizeLinear"(%132, %1963, %1964) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %1966 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1301> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1967 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1302> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1968 = torch.operator "onnx.DequantizeLinear"(%1965, %1966, %1967) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %1969 = torch.operator "onnx.Conv"(%1962, %830, %1968) {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<[2,128,?,?],f32>, !torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %1970 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1303> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1971 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1304> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1972 = torch.operator "onnx.QuantizeLinear"(%1969, %1970, %1971) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %1973 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1305> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1974 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1306> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1975 = torch.operator "onnx.DequantizeLinear"(%1972, %1973, %1974) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %1976 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1307> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1977 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1308> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1978 = torch.operator "onnx.QuantizeLinear"(%131, %1976, %1977) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %1979 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1309> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1980 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1310> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1981 = torch.operator "onnx.DequantizeLinear"(%1978, %1979, %1980) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %1982 = torch.operator "onnx.Mul"(%1975, %1981) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %1983 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1311> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1984 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1312> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1985 = torch.operator "onnx.QuantizeLinear"(%1982, %1983, %1984) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %1986 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1313> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1987 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1314> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1988 = torch.operator "onnx.DequantizeLinear"(%1985, %1986, %1987) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %1989 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1315> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1990 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1316> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1991 = torch.operator "onnx.QuantizeLinear"(%130, %1989, %1990) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %1992 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1317> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1993 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1318> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1994 = torch.operator "onnx.DequantizeLinear"(%1991, %1992, %1993) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %1995 = torch.operator "onnx.Add"(%1988, %1994) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %1996 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1319> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %1997 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1320> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %1998 = torch.operator "onnx.QuantizeLinear"(%1995, %1996, %1997) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %1999 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1321> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2000 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1322> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2001 = torch.operator "onnx.DequantizeLinear"(%1998, %1999, %2000) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2002 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1323> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2003 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1324> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2004 = torch.operator "onnx.QuantizeLinear"(%129, %2002, %2003) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %2005 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1325> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2006 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1326> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2007 = torch.operator "onnx.DequantizeLinear"(%2004, %2005, %2006) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %2008 = torch.operator "onnx.Conv"(%1882, %824, %2007) {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 = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[512,256,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2009 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1327> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2010 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1328> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2011 = torch.operator "onnx.QuantizeLinear"(%2008, %2009, %2010) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2012 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1329> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2013 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1330> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2014 = torch.operator "onnx.DequantizeLinear"(%2011, %2012, %2013) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2015 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1331> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2016 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1332> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2017 = torch.operator "onnx.QuantizeLinear"(%128, %2015, %2016) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2018 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1333> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2019 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1334> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2020 = torch.operator "onnx.DequantizeLinear"(%2017, %2018, %2019) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2021 = torch.operator "onnx.Mul"(%2014, %2020) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2022 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1335> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2023 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1336> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2024 = torch.operator "onnx.QuantizeLinear"(%2021, %2022, %2023) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2025 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1337> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2026 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1338> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2027 = torch.operator "onnx.DequantizeLinear"(%2024, %2025, %2026) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2028 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1339> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2029 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1340> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2030 = torch.operator "onnx.QuantizeLinear"(%127, %2028, %2029) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2031 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1341> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2032 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1342> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2033 = torch.operator "onnx.DequantizeLinear"(%2030, %2031, %2032) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2034 = torch.operator "onnx.Add"(%2027, %2033) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2035 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1343> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2036 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1344> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2037 = torch.operator "onnx.QuantizeLinear"(%2034, %2035, %2036) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2038 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1345> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2039 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1346> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2040 = torch.operator "onnx.DequantizeLinear"(%2037, %2038, %2039) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2041 = torch.operator "onnx.Add"(%2001, %2040) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2042 = torch.operator "onnx.Relu"(%2041) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2043 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1347> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2044 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1348> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2045 = torch.operator "onnx.QuantizeLinear"(%2042, %2043, %2044) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2046 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1349> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2047 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1350> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2048 = torch.operator "onnx.DequantizeLinear"(%2045, %2046, %2047) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2049 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1351> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2050 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1352> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2051 = torch.operator "onnx.QuantizeLinear"(%126, %2049, %2050) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %2052 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1353> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2053 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1354> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2054 = torch.operator "onnx.DequantizeLinear"(%2051, %2052, %2053) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %2055 = torch.operator "onnx.Conv"(%2048, %818, %2054) {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<[2,512,?,?],f32>, !torch.vtensor<[128,512,1,1],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2056 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1355> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2057 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1356> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2058 = torch.operator "onnx.QuantizeLinear"(%2055, %2056, %2057) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2059 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1357> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2060 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1358> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2061 = torch.operator "onnx.DequantizeLinear"(%2058, %2059, %2060) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2062 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1359> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2063 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1360> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2064 = torch.operator "onnx.QuantizeLinear"(%125, %2062, %2063) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2065 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1361> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2066 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1362> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2067 = torch.operator "onnx.DequantizeLinear"(%2064, %2065, %2066) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2068 = torch.operator "onnx.Mul"(%2061, %2067) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2069 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1363> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2070 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1364> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2071 = torch.operator "onnx.QuantizeLinear"(%2068, %2069, %2070) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2072 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1365> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2073 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1366> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2074 = torch.operator "onnx.DequantizeLinear"(%2071, %2072, %2073) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2075 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1367> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2076 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1368> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2077 = torch.operator "onnx.QuantizeLinear"(%124, %2075, %2076) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2078 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1369> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2079 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1370> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2080 = torch.operator "onnx.DequantizeLinear"(%2077, %2078, %2079) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2081 = torch.operator "onnx.Add"(%2074, %2080) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2082 = torch.operator "onnx.Relu"(%2081) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2083 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1371> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2084 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1372> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2085 = torch.operator "onnx.QuantizeLinear"(%2082, %2083, %2084) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2086 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1373> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2087 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1374> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2088 = torch.operator "onnx.DequantizeLinear"(%2085, %2086, %2087) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2089 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1375> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2090 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1376> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2091 = torch.operator "onnx.QuantizeLinear"(%123, %2089, %2090) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %2092 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1377> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2093 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1378> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2094 = torch.operator "onnx.DequantizeLinear"(%2091, %2092, %2093) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %2095 = torch.operator "onnx.Conv"(%2088, %812, %2094) {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<[2,128,?,?],f32>, !torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2096 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1379> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2097 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1380> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2098 = torch.operator "onnx.QuantizeLinear"(%2095, %2096, %2097) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2099 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1381> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2100 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1382> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2101 = torch.operator "onnx.DequantizeLinear"(%2098, %2099, %2100) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2102 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1383> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2103 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1384> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2104 = torch.operator "onnx.QuantizeLinear"(%122, %2102, %2103) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2105 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1385> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2106 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1386> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2107 = torch.operator "onnx.DequantizeLinear"(%2104, %2105, %2106) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2108 = torch.operator "onnx.Mul"(%2101, %2107) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2109 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1387> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2110 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1388> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2111 = torch.operator "onnx.QuantizeLinear"(%2108, %2109, %2110) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2112 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1389> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2113 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1390> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2114 = torch.operator "onnx.DequantizeLinear"(%2111, %2112, %2113) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2115 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1391> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2116 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1392> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2117 = torch.operator "onnx.QuantizeLinear"(%121, %2115, %2116) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2118 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1393> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2119 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1394> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2120 = torch.operator "onnx.DequantizeLinear"(%2117, %2118, %2119) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2121 = torch.operator "onnx.Add"(%2114, %2120) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2122 = torch.operator "onnx.Relu"(%2121) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2123 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1395> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2124 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1396> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2125 = torch.operator "onnx.QuantizeLinear"(%2122, %2123, %2124) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2126 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1397> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2127 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1398> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2128 = torch.operator "onnx.DequantizeLinear"(%2125, %2126, %2127) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2129 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1399> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2130 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1400> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2131 = torch.operator "onnx.QuantizeLinear"(%120, %2129, %2130) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %2132 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1401> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2133 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1402> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2134 = torch.operator "onnx.DequantizeLinear"(%2131, %2132, %2133) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %2135 = torch.operator "onnx.Conv"(%2128, %806, %2134) {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<[2,128,?,?],f32>, !torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2136 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1403> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2137 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1404> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2138 = torch.operator "onnx.QuantizeLinear"(%2135, %2136, %2137) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2139 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1405> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2140 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1406> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2141 = torch.operator "onnx.DequantizeLinear"(%2138, %2139, %2140) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2142 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1407> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2143 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1408> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2144 = torch.operator "onnx.QuantizeLinear"(%119, %2142, %2143) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2145 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1409> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2146 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1410> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2147 = torch.operator "onnx.DequantizeLinear"(%2144, %2145, %2146) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2148 = torch.operator "onnx.Mul"(%2141, %2147) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2149 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1411> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2150 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1412> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2151 = torch.operator "onnx.QuantizeLinear"(%2148, %2149, %2150) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2152 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1413> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2153 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1414> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2154 = torch.operator "onnx.DequantizeLinear"(%2151, %2152, %2153) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2155 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1415> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2156 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1416> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2157 = torch.operator "onnx.QuantizeLinear"(%118, %2155, %2156) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2158 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1417> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2159 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1418> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2160 = torch.operator "onnx.DequantizeLinear"(%2157, %2158, %2159) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2161 = torch.operator "onnx.Add"(%2154, %2160) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2162 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1419> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2163 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1420> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2164 = torch.operator "onnx.QuantizeLinear"(%2161, %2162, %2163) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2165 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1421> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2166 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1422> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2167 = torch.operator "onnx.DequantizeLinear"(%2164, %2165, %2166) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2168 = torch.operator "onnx.Add"(%2167, %2048) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2169 = torch.operator "onnx.Relu"(%2168) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2170 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1423> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2171 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1424> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2172 = torch.operator "onnx.QuantizeLinear"(%2169, %2170, %2171) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2173 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1425> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2174 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1426> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2175 = torch.operator "onnx.DequantizeLinear"(%2172, %2173, %2174) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2176 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1427> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2177 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1428> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2178 = torch.operator "onnx.QuantizeLinear"(%117, %2176, %2177) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %2179 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1429> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2180 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1430> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2181 = torch.operator "onnx.DequantizeLinear"(%2178, %2179, %2180) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %2182 = torch.operator "onnx.Conv"(%2175, %800, %2181) {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<[2,512,?,?],f32>, !torch.vtensor<[128,512,1,1],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2183 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1431> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2184 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1432> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2185 = torch.operator "onnx.QuantizeLinear"(%2182, %2183, %2184) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2186 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1433> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2187 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1434> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2188 = torch.operator "onnx.DequantizeLinear"(%2185, %2186, %2187) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2189 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1435> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2190 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1436> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2191 = torch.operator "onnx.QuantizeLinear"(%116, %2189, %2190) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2192 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1437> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2193 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1438> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2194 = torch.operator "onnx.DequantizeLinear"(%2191, %2192, %2193) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2195 = torch.operator "onnx.Mul"(%2188, %2194) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2196 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1439> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2197 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1440> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2198 = torch.operator "onnx.QuantizeLinear"(%2195, %2196, %2197) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2199 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1441> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2200 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1442> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2201 = torch.operator "onnx.DequantizeLinear"(%2198, %2199, %2200) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2202 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1443> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2203 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1444> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2204 = torch.operator "onnx.QuantizeLinear"(%115, %2202, %2203) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2205 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1445> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2206 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1446> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2207 = torch.operator "onnx.DequantizeLinear"(%2204, %2205, %2206) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2208 = torch.operator "onnx.Add"(%2201, %2207) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2209 = torch.operator "onnx.Relu"(%2208) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2210 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1447> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2211 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1448> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2212 = torch.operator "onnx.QuantizeLinear"(%2209, %2210, %2211) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2213 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1449> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2214 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1450> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2215 = torch.operator "onnx.DequantizeLinear"(%2212, %2213, %2214) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2216 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1451> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2217 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1452> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2218 = torch.operator "onnx.QuantizeLinear"(%114, %2216, %2217) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %2219 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1453> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2220 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1454> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2221 = torch.operator "onnx.DequantizeLinear"(%2218, %2219, %2220) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %2222 = torch.operator "onnx.Conv"(%2215, %794, %2221) {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<[2,128,?,?],f32>, !torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2223 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1455> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2224 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1456> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2225 = torch.operator "onnx.QuantizeLinear"(%2222, %2223, %2224) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2226 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1457> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2227 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1458> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2228 = torch.operator "onnx.DequantizeLinear"(%2225, %2226, %2227) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2229 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1459> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2230 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1460> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2231 = torch.operator "onnx.QuantizeLinear"(%113, %2229, %2230) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2232 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1461> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2233 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1462> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2234 = torch.operator "onnx.DequantizeLinear"(%2231, %2232, %2233) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2235 = torch.operator "onnx.Mul"(%2228, %2234) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2236 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1463> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2237 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1464> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2238 = torch.operator "onnx.QuantizeLinear"(%2235, %2236, %2237) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2239 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1465> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2240 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1466> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2241 = torch.operator "onnx.DequantizeLinear"(%2238, %2239, %2240) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2242 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1467> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2243 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1468> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2244 = torch.operator "onnx.QuantizeLinear"(%112, %2242, %2243) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2245 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1469> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2246 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1470> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2247 = torch.operator "onnx.DequantizeLinear"(%2244, %2245, %2246) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2248 = torch.operator "onnx.Add"(%2241, %2247) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2249 = torch.operator "onnx.Relu"(%2248) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2250 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1471> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2251 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1472> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2252 = torch.operator "onnx.QuantizeLinear"(%2249, %2250, %2251) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2253 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1473> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2254 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1474> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2255 = torch.operator "onnx.DequantizeLinear"(%2252, %2253, %2254) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2256 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1475> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2257 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1476> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2258 = torch.operator "onnx.QuantizeLinear"(%111, %2256, %2257) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %2259 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1477> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2260 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1478> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2261 = torch.operator "onnx.DequantizeLinear"(%2258, %2259, %2260) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %2262 = torch.operator "onnx.Conv"(%2255, %788, %2261) {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<[2,128,?,?],f32>, !torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2263 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1479> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2264 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1480> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2265 = torch.operator "onnx.QuantizeLinear"(%2262, %2263, %2264) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2266 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1481> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2267 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1482> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2268 = torch.operator "onnx.DequantizeLinear"(%2265, %2266, %2267) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2269 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1483> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2270 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1484> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2271 = torch.operator "onnx.QuantizeLinear"(%110, %2269, %2270) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2272 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1485> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2273 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1486> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2274 = torch.operator "onnx.DequantizeLinear"(%2271, %2272, %2273) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2275 = torch.operator "onnx.Mul"(%2268, %2274) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2276 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1487> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2277 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1488> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2278 = torch.operator "onnx.QuantizeLinear"(%2275, %2276, %2277) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2279 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1489> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2280 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1490> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2281 = torch.operator "onnx.DequantizeLinear"(%2278, %2279, %2280) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2282 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1491> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2283 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1492> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2284 = torch.operator "onnx.QuantizeLinear"(%109, %2282, %2283) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2285 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1493> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2286 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1494> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2287 = torch.operator "onnx.DequantizeLinear"(%2284, %2285, %2286) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2288 = torch.operator "onnx.Add"(%2281, %2287) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2289 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1495> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2290 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1496> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2291 = torch.operator "onnx.QuantizeLinear"(%2288, %2289, %2290) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2292 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1497> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2293 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1498> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2294 = torch.operator "onnx.DequantizeLinear"(%2291, %2292, %2293) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2295 = torch.operator "onnx.Add"(%2294, %2175) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2296 = torch.operator "onnx.Relu"(%2295) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2297 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1499> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2298 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1500> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2299 = torch.operator "onnx.QuantizeLinear"(%2296, %2297, %2298) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2300 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1501> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2301 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1502> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2302 = torch.operator "onnx.DequantizeLinear"(%2299, %2300, %2301) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2303 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1503> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2304 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1504> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2305 = torch.operator "onnx.QuantizeLinear"(%108, %2303, %2304) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %2306 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1505> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2307 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1506> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2308 = torch.operator "onnx.DequantizeLinear"(%2305, %2306, %2307) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %2309 = torch.operator "onnx.Conv"(%2302, %782, %2308) {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<[2,512,?,?],f32>, !torch.vtensor<[128,512,1,1],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2310 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1507> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2311 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1508> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2312 = torch.operator "onnx.QuantizeLinear"(%2309, %2310, %2311) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2313 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1509> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2314 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1510> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2315 = torch.operator "onnx.DequantizeLinear"(%2312, %2313, %2314) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2316 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1511> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2317 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1512> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2318 = torch.operator "onnx.QuantizeLinear"(%107, %2316, %2317) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2319 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1513> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2320 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1514> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2321 = torch.operator "onnx.DequantizeLinear"(%2318, %2319, %2320) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2322 = torch.operator "onnx.Mul"(%2315, %2321) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2323 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1515> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2324 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1516> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2325 = torch.operator "onnx.QuantizeLinear"(%2322, %2323, %2324) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2326 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1517> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2327 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1518> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2328 = torch.operator "onnx.DequantizeLinear"(%2325, %2326, %2327) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2329 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1519> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2330 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1520> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2331 = torch.operator "onnx.QuantizeLinear"(%106, %2329, %2330) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2332 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1521> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2333 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1522> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2334 = torch.operator "onnx.DequantizeLinear"(%2331, %2332, %2333) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2335 = torch.operator "onnx.Add"(%2328, %2334) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2336 = torch.operator "onnx.Relu"(%2335) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2337 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1523> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2338 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1524> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2339 = torch.operator "onnx.QuantizeLinear"(%2336, %2337, %2338) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2340 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1525> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2341 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1526> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2342 = torch.operator "onnx.DequantizeLinear"(%2339, %2340, %2341) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2343 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1527> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2344 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1528> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2345 = torch.operator "onnx.QuantizeLinear"(%105, %2343, %2344) : (!torch.vtensor<[128],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],si8> 
    %2346 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1529> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2347 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1530> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2348 = torch.operator "onnx.DequantizeLinear"(%2345, %2346, %2347) : (!torch.vtensor<[128],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[128],f32> 
    %2349 = torch.operator "onnx.Conv"(%2342, %776, %2348) {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<[2,128,?,?],f32>, !torch.vtensor<[128,128,3,3],f32>, !torch.vtensor<[128],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2350 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1531> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2351 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1532> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2352 = torch.operator "onnx.QuantizeLinear"(%2349, %2350, %2351) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2353 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1533> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2354 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1534> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2355 = torch.operator "onnx.DequantizeLinear"(%2352, %2353, %2354) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2356 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1535> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2357 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1536> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2358 = torch.operator "onnx.QuantizeLinear"(%104, %2356, %2357) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2359 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1537> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2360 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1538> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2361 = torch.operator "onnx.DequantizeLinear"(%2358, %2359, %2360) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2362 = torch.operator "onnx.Mul"(%2355, %2361) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2363 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1539> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2364 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1540> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2365 = torch.operator "onnx.QuantizeLinear"(%2362, %2363, %2364) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2366 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1541> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2367 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1542> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2368 = torch.operator "onnx.DequantizeLinear"(%2365, %2366, %2367) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2369 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1543> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2370 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1544> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2371 = torch.operator "onnx.QuantizeLinear"(%103, %2369, %2370) : (!torch.vtensor<[1,128,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],si8> 
    %2372 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1545> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2373 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1546> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2374 = torch.operator "onnx.DequantizeLinear"(%2371, %2372, %2373) : (!torch.vtensor<[1,128,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,128,1,1],f32> 
    %2375 = torch.operator "onnx.Add"(%2368, %2374) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[1,128,1,1],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2376 = torch.operator "onnx.Relu"(%2375) : (!torch.vtensor<[2,128,?,?],f32>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2377 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1547> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2378 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1548> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2379 = torch.operator "onnx.QuantizeLinear"(%2376, %2377, %2378) : (!torch.vtensor<[2,128,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],si8> 
    %2380 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1549> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2381 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1550> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2382 = torch.operator "onnx.DequantizeLinear"(%2379, %2380, %2381) : (!torch.vtensor<[2,128,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,128,?,?],f32> 
    %2383 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1551> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2384 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1552> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2385 = torch.operator "onnx.QuantizeLinear"(%102, %2383, %2384) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %2386 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1553> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2387 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1554> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2388 = torch.operator "onnx.DequantizeLinear"(%2385, %2386, %2387) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %2389 = torch.operator "onnx.Conv"(%2382, %770, %2388) {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<[2,128,?,?],f32>, !torch.vtensor<[512,128,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2390 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1555> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2391 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1556> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2392 = torch.operator "onnx.QuantizeLinear"(%2389, %2390, %2391) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2393 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1557> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2394 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1558> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2395 = torch.operator "onnx.DequantizeLinear"(%2392, %2393, %2394) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2396 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1559> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2397 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1560> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2398 = torch.operator "onnx.QuantizeLinear"(%101, %2396, %2397) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2399 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1561> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2400 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1562> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2401 = torch.operator "onnx.DequantizeLinear"(%2398, %2399, %2400) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2402 = torch.operator "onnx.Mul"(%2395, %2401) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2403 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1563> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2404 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1564> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2405 = torch.operator "onnx.QuantizeLinear"(%2402, %2403, %2404) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2406 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1565> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2407 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1566> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2408 = torch.operator "onnx.DequantizeLinear"(%2405, %2406, %2407) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2409 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1567> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2410 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1568> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2411 = torch.operator "onnx.QuantizeLinear"(%100, %2409, %2410) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %2412 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1569> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2413 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1570> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2414 = torch.operator "onnx.DequantizeLinear"(%2411, %2412, %2413) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %2415 = torch.operator "onnx.Add"(%2408, %2414) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2416 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1571> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2417 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1572> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2418 = torch.operator "onnx.QuantizeLinear"(%2415, %2416, %2417) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2419 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1573> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2420 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1574> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2421 = torch.operator "onnx.DequantizeLinear"(%2418, %2419, %2420) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2422 = torch.operator "onnx.Add"(%2421, %2302) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2423 = torch.operator "onnx.Relu"(%2422) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2424 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1575> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2425 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1576> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2426 = torch.operator "onnx.QuantizeLinear"(%2423, %2424, %2425) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %2427 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1577> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2428 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1578> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2429 = torch.operator "onnx.DequantizeLinear"(%2426, %2427, %2428) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %2430 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1579> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2431 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1580> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2432 = torch.operator "onnx.QuantizeLinear"(%99, %2430, %2431) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2433 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1581> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2434 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1582> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2435 = torch.operator "onnx.DequantizeLinear"(%2432, %2433, %2434) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2436 = torch.operator "onnx.Conv"(%2429, %764, %2435) {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<[2,512,?,?],f32>, !torch.vtensor<[256,512,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2437 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1583> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2438 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1584> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2439 = torch.operator "onnx.QuantizeLinear"(%2436, %2437, %2438) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2440 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1585> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2441 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1586> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2442 = torch.operator "onnx.DequantizeLinear"(%2439, %2440, %2441) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2443 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1587> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2444 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1588> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2445 = torch.operator "onnx.QuantizeLinear"(%98, %2443, %2444) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2446 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1589> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2447 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1590> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2448 = torch.operator "onnx.DequantizeLinear"(%2445, %2446, %2447) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2449 = torch.operator "onnx.Mul"(%2442, %2448) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2450 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1591> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2451 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1592> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2452 = torch.operator "onnx.QuantizeLinear"(%2449, %2450, %2451) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2453 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1593> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2454 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1594> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2455 = torch.operator "onnx.DequantizeLinear"(%2452, %2453, %2454) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2456 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1595> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2457 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1596> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2458 = torch.operator "onnx.QuantizeLinear"(%97, %2456, %2457) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2459 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1597> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2460 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1598> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2461 = torch.operator "onnx.DequantizeLinear"(%2458, %2459, %2460) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2462 = torch.operator "onnx.Add"(%2455, %2461) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2463 = torch.operator "onnx.Relu"(%2462) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2464 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1599> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2465 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1600> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2466 = torch.operator "onnx.QuantizeLinear"(%2463, %2464, %2465) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2467 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1601> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2468 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1602> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2469 = torch.operator "onnx.DequantizeLinear"(%2466, %2467, %2468) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2470 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1603> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2471 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1604> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2472 = torch.operator "onnx.QuantizeLinear"(%96, %2470, %2471) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2473 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1605> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2474 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1606> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2475 = torch.operator "onnx.DequantizeLinear"(%2472, %2473, %2474) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2476 = torch.operator "onnx.Conv"(%2469, %758, %2475) {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 = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2477 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1607> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2478 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1608> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2479 = torch.operator "onnx.QuantizeLinear"(%2476, %2477, %2478) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2480 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1609> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2481 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1610> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2482 = torch.operator "onnx.DequantizeLinear"(%2479, %2480, %2481) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2483 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1611> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2484 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1612> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2485 = torch.operator "onnx.QuantizeLinear"(%95, %2483, %2484) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2486 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1613> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2487 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1614> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2488 = torch.operator "onnx.DequantizeLinear"(%2485, %2486, %2487) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2489 = torch.operator "onnx.Mul"(%2482, %2488) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2490 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1615> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2491 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1616> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2492 = torch.operator "onnx.QuantizeLinear"(%2489, %2490, %2491) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2493 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1617> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2494 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1618> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2495 = torch.operator "onnx.DequantizeLinear"(%2492, %2493, %2494) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2496 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1619> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2497 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1620> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2498 = torch.operator "onnx.QuantizeLinear"(%94, %2496, %2497) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2499 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1621> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2500 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1622> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2501 = torch.operator "onnx.DequantizeLinear"(%2498, %2499, %2500) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2502 = torch.operator "onnx.Add"(%2495, %2501) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2503 = torch.operator "onnx.Relu"(%2502) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2504 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1623> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2505 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1624> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2506 = torch.operator "onnx.QuantizeLinear"(%2503, %2504, %2505) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2507 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1625> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2508 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1626> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2509 = torch.operator "onnx.DequantizeLinear"(%2506, %2507, %2508) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2510 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1627> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2511 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1628> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2512 = torch.operator "onnx.QuantizeLinear"(%93, %2510, %2511) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %2513 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1629> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2514 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1630> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2515 = torch.operator "onnx.DequantizeLinear"(%2512, %2513, %2514) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %2516 = torch.operator "onnx.Conv"(%2509, %752, %2515) {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<[2,256,?,?],f32>, !torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[1024],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2517 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1631> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2518 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1632> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2519 = torch.operator "onnx.QuantizeLinear"(%2516, %2517, %2518) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2520 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1633> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2521 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1634> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2522 = torch.operator "onnx.DequantizeLinear"(%2519, %2520, %2521) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2523 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1635> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2524 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1636> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2525 = torch.operator "onnx.QuantizeLinear"(%92, %2523, %2524) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2526 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1637> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2527 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1638> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2528 = torch.operator "onnx.DequantizeLinear"(%2525, %2526, %2527) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2529 = torch.operator "onnx.Mul"(%2522, %2528) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2530 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1639> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2531 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1640> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2532 = torch.operator "onnx.QuantizeLinear"(%2529, %2530, %2531) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2533 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1641> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2534 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1642> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2535 = torch.operator "onnx.DequantizeLinear"(%2532, %2533, %2534) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2536 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1643> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2537 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1644> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2538 = torch.operator "onnx.QuantizeLinear"(%91, %2536, %2537) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2539 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1645> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2540 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1646> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2541 = torch.operator "onnx.DequantizeLinear"(%2538, %2539, %2540) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2542 = torch.operator "onnx.Add"(%2535, %2541) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2543 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1647> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2544 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1648> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2545 = torch.operator "onnx.QuantizeLinear"(%2542, %2543, %2544) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2546 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1649> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2547 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1650> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2548 = torch.operator "onnx.DequantizeLinear"(%2545, %2546, %2547) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2549 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1651> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2550 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1652> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2551 = torch.operator "onnx.QuantizeLinear"(%90, %2549, %2550) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %2552 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1653> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2553 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1654> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2554 = torch.operator "onnx.DequantizeLinear"(%2551, %2552, %2553) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %2555 = torch.operator "onnx.Conv"(%2429, %746, %2554) {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 = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1024,512,1,1],f32>, !torch.vtensor<[1024],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2556 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1655> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2557 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1656> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2558 = torch.operator "onnx.QuantizeLinear"(%2555, %2556, %2557) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2559 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1657> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2560 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1658> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2561 = torch.operator "onnx.DequantizeLinear"(%2558, %2559, %2560) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2562 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1659> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2563 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1660> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2564 = torch.operator "onnx.QuantizeLinear"(%89, %2562, %2563) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2565 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1661> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2566 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1662> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2567 = torch.operator "onnx.DequantizeLinear"(%2564, %2565, %2566) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2568 = torch.operator "onnx.Mul"(%2561, %2567) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2569 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1663> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2570 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1664> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2571 = torch.operator "onnx.QuantizeLinear"(%2568, %2569, %2570) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2572 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1665> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2573 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1666> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2574 = torch.operator "onnx.DequantizeLinear"(%2571, %2572, %2573) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2575 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1667> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2576 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1668> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2577 = torch.operator "onnx.QuantizeLinear"(%88, %2575, %2576) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2578 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1669> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2579 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1670> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2580 = torch.operator "onnx.DequantizeLinear"(%2577, %2578, %2579) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2581 = torch.operator "onnx.Add"(%2574, %2580) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2582 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1671> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2583 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1672> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2584 = torch.operator "onnx.QuantizeLinear"(%2581, %2582, %2583) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2585 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1673> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2586 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1674> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2587 = torch.operator "onnx.DequantizeLinear"(%2584, %2585, %2586) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2588 = torch.operator "onnx.Add"(%2548, %2587) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2589 = torch.operator "onnx.Relu"(%2588) : (!torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2590 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1675> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2591 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1676> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2592 = torch.operator "onnx.QuantizeLinear"(%2589, %2590, %2591) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2593 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1677> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2594 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1678> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2595 = torch.operator "onnx.DequantizeLinear"(%2592, %2593, %2594) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2596 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1679> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2597 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1680> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2598 = torch.operator "onnx.QuantizeLinear"(%87, %2596, %2597) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2599 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1681> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2600 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1682> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2601 = torch.operator "onnx.DequantizeLinear"(%2598, %2599, %2600) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2602 = torch.operator "onnx.Conv"(%2595, %740, %2601) {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<[2,1024,?,?],f32>, !torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2603 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1683> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2604 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1684> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2605 = torch.operator "onnx.QuantizeLinear"(%2602, %2603, %2604) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2606 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1685> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2607 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1686> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2608 = torch.operator "onnx.DequantizeLinear"(%2605, %2606, %2607) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2609 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1687> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2610 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1688> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2611 = torch.operator "onnx.QuantizeLinear"(%86, %2609, %2610) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2612 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1689> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2613 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1690> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2614 = torch.operator "onnx.DequantizeLinear"(%2611, %2612, %2613) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2615 = torch.operator "onnx.Mul"(%2608, %2614) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2616 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1691> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2617 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1692> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2618 = torch.operator "onnx.QuantizeLinear"(%2615, %2616, %2617) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2619 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1693> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2620 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1694> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2621 = torch.operator "onnx.DequantizeLinear"(%2618, %2619, %2620) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2622 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1695> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2623 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1696> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2624 = torch.operator "onnx.QuantizeLinear"(%85, %2622, %2623) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2625 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1697> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2626 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1698> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2627 = torch.operator "onnx.DequantizeLinear"(%2624, %2625, %2626) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2628 = torch.operator "onnx.Add"(%2621, %2627) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2629 = torch.operator "onnx.Relu"(%2628) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2630 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1699> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2631 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1700> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2632 = torch.operator "onnx.QuantizeLinear"(%2629, %2630, %2631) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2633 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1701> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2634 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1702> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2635 = torch.operator "onnx.DequantizeLinear"(%2632, %2633, %2634) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2636 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1703> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2637 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1704> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2638 = torch.operator "onnx.QuantizeLinear"(%84, %2636, %2637) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2639 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1705> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2640 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1706> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2641 = torch.operator "onnx.DequantizeLinear"(%2638, %2639, %2640) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2642 = torch.operator "onnx.Conv"(%2635, %734, %2641) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2643 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1707> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2644 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1708> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2645 = torch.operator "onnx.QuantizeLinear"(%2642, %2643, %2644) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2646 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1709> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2647 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1710> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2648 = torch.operator "onnx.DequantizeLinear"(%2645, %2646, %2647) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2649 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1711> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2650 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1712> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2651 = torch.operator "onnx.QuantizeLinear"(%83, %2649, %2650) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1713> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2653 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1714> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2654 = torch.operator "onnx.DequantizeLinear"(%2651, %2652, %2653) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2655 = torch.operator "onnx.Mul"(%2648, %2654) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2656 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1715> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2657 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1716> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2658 = torch.operator "onnx.QuantizeLinear"(%2655, %2656, %2657) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2659 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1717> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2660 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1718> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2661 = torch.operator "onnx.DequantizeLinear"(%2658, %2659, %2660) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2662 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1719> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2663 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1720> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2664 = torch.operator "onnx.QuantizeLinear"(%82, %2662, %2663) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2665 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1721> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1722> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2667 = torch.operator "onnx.DequantizeLinear"(%2664, %2665, %2666) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2668 = torch.operator "onnx.Add"(%2661, %2667) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2669 = torch.operator "onnx.Relu"(%2668) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2670 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1723> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2671 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1724> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2672 = torch.operator "onnx.QuantizeLinear"(%2669, %2670, %2671) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2673 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1725> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2674 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1726> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2675 = torch.operator "onnx.DequantizeLinear"(%2672, %2673, %2674) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2676 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1727> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2677 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1728> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2678 = torch.operator "onnx.QuantizeLinear"(%81, %2676, %2677) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %2679 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1729> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2680 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1730> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2681 = torch.operator "onnx.DequantizeLinear"(%2678, %2679, %2680) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %2682 = torch.operator "onnx.Conv"(%2675, %728, %2681) {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<[2,256,?,?],f32>, !torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[1024],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2683 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1731> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2684 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1732> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2685 = torch.operator "onnx.QuantizeLinear"(%2682, %2683, %2684) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2686 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1733> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2687 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1734> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2688 = torch.operator "onnx.DequantizeLinear"(%2685, %2686, %2687) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2689 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1735> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2690 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1736> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2691 = torch.operator "onnx.QuantizeLinear"(%80, %2689, %2690) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2692 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1737> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2693 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1738> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2694 = torch.operator "onnx.DequantizeLinear"(%2691, %2692, %2693) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2695 = torch.operator "onnx.Mul"(%2688, %2694) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2696 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1739> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2697 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1740> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2698 = torch.operator "onnx.QuantizeLinear"(%2695, %2696, %2697) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2699 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1741> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2700 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1742> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2701 = torch.operator "onnx.DequantizeLinear"(%2698, %2699, %2700) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2702 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1743> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2703 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1744> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2704 = torch.operator "onnx.QuantizeLinear"(%79, %2702, %2703) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2705 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1745> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2706 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1746> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2707 = torch.operator "onnx.DequantizeLinear"(%2704, %2705, %2706) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2708 = torch.operator "onnx.Add"(%2701, %2707) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2709 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1747> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2710 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1748> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2711 = torch.operator "onnx.QuantizeLinear"(%2708, %2709, %2710) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2712 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1749> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2713 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1750> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2714 = torch.operator "onnx.DequantizeLinear"(%2711, %2712, %2713) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2715 = torch.operator "onnx.Add"(%2714, %2595) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2716 = torch.operator "onnx.Relu"(%2715) : (!torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2717 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1751> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2718 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1752> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2719 = torch.operator "onnx.QuantizeLinear"(%2716, %2717, %2718) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2720 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1753> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2721 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1754> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2722 = torch.operator "onnx.DequantizeLinear"(%2719, %2720, %2721) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2723 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1755> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2724 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1756> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2725 = torch.operator "onnx.QuantizeLinear"(%78, %2723, %2724) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2726 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1757> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2727 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1758> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2728 = torch.operator "onnx.DequantizeLinear"(%2725, %2726, %2727) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2729 = torch.operator "onnx.Conv"(%2722, %722, %2728) {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<[2,1024,?,?],f32>, !torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2730 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1759> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2731 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1760> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2732 = torch.operator "onnx.QuantizeLinear"(%2729, %2730, %2731) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2733 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1761> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2734 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1762> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2735 = torch.operator "onnx.DequantizeLinear"(%2732, %2733, %2734) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2736 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1763> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2737 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1764> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2738 = torch.operator "onnx.QuantizeLinear"(%77, %2736, %2737) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2739 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1765> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2740 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1766> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2741 = torch.operator "onnx.DequantizeLinear"(%2738, %2739, %2740) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2742 = torch.operator "onnx.Mul"(%2735, %2741) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2743 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1767> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2744 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1768> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2745 = torch.operator "onnx.QuantizeLinear"(%2742, %2743, %2744) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2746 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1769> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2747 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1770> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2748 = torch.operator "onnx.DequantizeLinear"(%2745, %2746, %2747) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2749 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1771> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2750 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1772> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2751 = torch.operator "onnx.QuantizeLinear"(%76, %2749, %2750) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2752 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1773> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2753 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1774> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2754 = torch.operator "onnx.DequantizeLinear"(%2751, %2752, %2753) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2755 = torch.operator "onnx.Add"(%2748, %2754) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2756 = torch.operator "onnx.Relu"(%2755) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2757 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1775> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2758 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1776> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2759 = torch.operator "onnx.QuantizeLinear"(%2756, %2757, %2758) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2760 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1777> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2761 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1778> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2762 = torch.operator "onnx.DequantizeLinear"(%2759, %2760, %2761) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2763 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1779> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2764 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1780> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2765 = torch.operator "onnx.QuantizeLinear"(%75, %2763, %2764) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2766 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1781> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2767 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1782> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2768 = torch.operator "onnx.DequantizeLinear"(%2765, %2766, %2767) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2769 = torch.operator "onnx.Conv"(%2762, %716, %2768) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2770 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1783> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2771 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1784> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2772 = torch.operator "onnx.QuantizeLinear"(%2769, %2770, %2771) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2773 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1785> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2774 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1786> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2775 = torch.operator "onnx.DequantizeLinear"(%2772, %2773, %2774) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2776 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1787> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2777 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1788> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2778 = torch.operator "onnx.QuantizeLinear"(%74, %2776, %2777) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2779 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1789> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2780 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1790> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2781 = torch.operator "onnx.DequantizeLinear"(%2778, %2779, %2780) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2782 = torch.operator "onnx.Mul"(%2775, %2781) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2783 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1791> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2784 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1792> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2785 = torch.operator "onnx.QuantizeLinear"(%2782, %2783, %2784) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2786 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1793> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2787 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1794> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2788 = torch.operator "onnx.DequantizeLinear"(%2785, %2786, %2787) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2789 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1795> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2790 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1796> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2791 = torch.operator "onnx.QuantizeLinear"(%73, %2789, %2790) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2792 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1797> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2793 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1798> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2794 = torch.operator "onnx.DequantizeLinear"(%2791, %2792, %2793) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2795 = torch.operator "onnx.Add"(%2788, %2794) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2796 = torch.operator "onnx.Relu"(%2795) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2797 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1799> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2798 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1800> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2799 = torch.operator "onnx.QuantizeLinear"(%2796, %2797, %2798) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2800 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1801> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2801 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1802> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2802 = torch.operator "onnx.DequantizeLinear"(%2799, %2800, %2801) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2803 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1803> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2804 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1804> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2805 = torch.operator "onnx.QuantizeLinear"(%72, %2803, %2804) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %2806 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1805> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2807 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1806> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2808 = torch.operator "onnx.DequantizeLinear"(%2805, %2806, %2807) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %2809 = torch.operator "onnx.Conv"(%2802, %710, %2808) {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<[2,256,?,?],f32>, !torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[1024],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2810 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1807> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2811 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1808> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2812 = torch.operator "onnx.QuantizeLinear"(%2809, %2810, %2811) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2813 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1809> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2814 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1810> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2815 = torch.operator "onnx.DequantizeLinear"(%2812, %2813, %2814) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2816 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1811> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2817 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1812> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2818 = torch.operator "onnx.QuantizeLinear"(%71, %2816, %2817) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2819 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1813> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2820 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1814> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2821 = torch.operator "onnx.DequantizeLinear"(%2818, %2819, %2820) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2822 = torch.operator "onnx.Mul"(%2815, %2821) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2823 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1815> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2824 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1816> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2825 = torch.operator "onnx.QuantizeLinear"(%2822, %2823, %2824) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2826 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1817> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2827 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1818> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2828 = torch.operator "onnx.DequantizeLinear"(%2825, %2826, %2827) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2829 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1819> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2830 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1820> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2831 = torch.operator "onnx.QuantizeLinear"(%70, %2829, %2830) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2832 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1821> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2833 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1822> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2834 = torch.operator "onnx.DequantizeLinear"(%2831, %2832, %2833) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2835 = torch.operator "onnx.Add"(%2828, %2834) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2836 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1823> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2837 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1824> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2838 = torch.operator "onnx.QuantizeLinear"(%2835, %2836, %2837) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2839 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1825> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2840 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1826> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2841 = torch.operator "onnx.DequantizeLinear"(%2838, %2839, %2840) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2842 = torch.operator "onnx.Add"(%2841, %2722) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2843 = torch.operator "onnx.Relu"(%2842) : (!torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2844 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1827> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2845 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1828> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2846 = torch.operator "onnx.QuantizeLinear"(%2843, %2844, %2845) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2847 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1829> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2848 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1830> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2849 = torch.operator "onnx.DequantizeLinear"(%2846, %2847, %2848) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2850 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1831> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2851 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1832> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2852 = torch.operator "onnx.QuantizeLinear"(%69, %2850, %2851) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2853 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1833> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2854 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1834> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2855 = torch.operator "onnx.DequantizeLinear"(%2852, %2853, %2854) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2856 = torch.operator "onnx.Conv"(%2849, %704, %2855) {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<[2,1024,?,?],f32>, !torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2857 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1835> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2858 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1836> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2859 = torch.operator "onnx.QuantizeLinear"(%2856, %2857, %2858) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2860 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1837> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2861 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1838> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2862 = torch.operator "onnx.DequantizeLinear"(%2859, %2860, %2861) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2863 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1839> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2864 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1840> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2865 = torch.operator "onnx.QuantizeLinear"(%68, %2863, %2864) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2866 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1841> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2867 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1842> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2868 = torch.operator "onnx.DequantizeLinear"(%2865, %2866, %2867) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2869 = torch.operator "onnx.Mul"(%2862, %2868) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2870 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1843> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2871 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1844> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2872 = torch.operator "onnx.QuantizeLinear"(%2869, %2870, %2871) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2873 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1845> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2874 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1846> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2875 = torch.operator "onnx.DequantizeLinear"(%2872, %2873, %2874) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2876 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1847> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2877 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1848> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2878 = torch.operator "onnx.QuantizeLinear"(%67, %2876, %2877) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2879 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1849> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2880 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1850> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2881 = torch.operator "onnx.DequantizeLinear"(%2878, %2879, %2880) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2882 = torch.operator "onnx.Add"(%2875, %2881) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2883 = torch.operator "onnx.Relu"(%2882) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2884 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1851> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2885 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1852> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2886 = torch.operator "onnx.QuantizeLinear"(%2883, %2884, %2885) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2887 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1853> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2888 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1854> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2889 = torch.operator "onnx.DequantizeLinear"(%2886, %2887, %2888) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2890 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1855> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2891 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1856> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2892 = torch.operator "onnx.QuantizeLinear"(%66, %2890, %2891) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2893 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1857> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2894 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1858> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2895 = torch.operator "onnx.DequantizeLinear"(%2892, %2893, %2894) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2896 = torch.operator "onnx.Conv"(%2889, %698, %2895) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2897 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1859> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2898 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1860> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2899 = torch.operator "onnx.QuantizeLinear"(%2896, %2897, %2898) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2900 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1861> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2901 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1862> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2902 = torch.operator "onnx.DequantizeLinear"(%2899, %2900, %2901) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2903 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1863> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2904 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1864> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2905 = torch.operator "onnx.QuantizeLinear"(%65, %2903, %2904) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2906 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1865> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2907 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1866> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2908 = torch.operator "onnx.DequantizeLinear"(%2905, %2906, %2907) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2909 = torch.operator "onnx.Mul"(%2902, %2908) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2910 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1867> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2911 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1868> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2912 = torch.operator "onnx.QuantizeLinear"(%2909, %2910, %2911) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2913 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1869> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2914 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1870> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2915 = torch.operator "onnx.DequantizeLinear"(%2912, %2913, %2914) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2916 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1871> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2917 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1872> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2918 = torch.operator "onnx.QuantizeLinear"(%64, %2916, %2917) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2919 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1873> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2920 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1874> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2921 = torch.operator "onnx.DequantizeLinear"(%2918, %2919, %2920) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2922 = torch.operator "onnx.Add"(%2915, %2921) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2923 = torch.operator "onnx.Relu"(%2922) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2924 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1875> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2925 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1876> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2926 = torch.operator "onnx.QuantizeLinear"(%2923, %2924, %2925) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2927 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1877> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2928 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1878> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2929 = torch.operator "onnx.DequantizeLinear"(%2926, %2927, %2928) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2930 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1879> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2931 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1880> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2932 = torch.operator "onnx.QuantizeLinear"(%63, %2930, %2931) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %2933 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1881> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2934 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1882> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2935 = torch.operator "onnx.DequantizeLinear"(%2932, %2933, %2934) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %2936 = torch.operator "onnx.Conv"(%2929, %692, %2935) {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<[2,256,?,?],f32>, !torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[1024],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2937 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1883> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2938 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1884> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2939 = torch.operator "onnx.QuantizeLinear"(%2936, %2937, %2938) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2940 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1885> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2941 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1886> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2942 = torch.operator "onnx.DequantizeLinear"(%2939, %2940, %2941) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2943 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1887> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2944 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1888> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2945 = torch.operator "onnx.QuantizeLinear"(%62, %2943, %2944) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2946 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1889> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2947 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1890> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2948 = torch.operator "onnx.DequantizeLinear"(%2945, %2946, %2947) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2949 = torch.operator "onnx.Mul"(%2942, %2948) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2950 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1891> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2951 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1892> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2952 = torch.operator "onnx.QuantizeLinear"(%2949, %2950, %2951) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2953 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1893> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2954 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1894> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2955 = torch.operator "onnx.DequantizeLinear"(%2952, %2953, %2954) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2956 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1895> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2957 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1896> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2958 = torch.operator "onnx.QuantizeLinear"(%61, %2956, %2957) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %2959 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1897> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2960 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1898> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2961 = torch.operator "onnx.DequantizeLinear"(%2958, %2959, %2960) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %2962 = torch.operator "onnx.Add"(%2955, %2961) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2963 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1899> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2964 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1900> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2965 = torch.operator "onnx.QuantizeLinear"(%2962, %2963, %2964) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2966 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1901> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2967 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1902> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2968 = torch.operator "onnx.DequantizeLinear"(%2965, %2966, %2967) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2969 = torch.operator "onnx.Add"(%2968, %2849) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2970 = torch.operator "onnx.Relu"(%2969) : (!torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2971 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1903> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2972 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1904> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2973 = torch.operator "onnx.QuantizeLinear"(%2970, %2971, %2972) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %2974 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1905> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2975 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1906> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2976 = torch.operator "onnx.DequantizeLinear"(%2973, %2974, %2975) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %2977 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1907> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2978 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1908> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2979 = torch.operator "onnx.QuantizeLinear"(%60, %2977, %2978) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %2980 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1909> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2981 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1910> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2982 = torch.operator "onnx.DequantizeLinear"(%2979, %2980, %2981) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %2983 = torch.operator "onnx.Conv"(%2976, %686, %2982) {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<[2,1024,?,?],f32>, !torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2984 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1911> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2985 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1912> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2986 = torch.operator "onnx.QuantizeLinear"(%2983, %2984, %2985) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %2987 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1913> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2988 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1914> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2989 = torch.operator "onnx.DequantizeLinear"(%2986, %2987, %2988) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2990 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1915> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2991 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1916> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2992 = torch.operator "onnx.QuantizeLinear"(%59, %2990, %2991) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %2993 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1917> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2994 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1918> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2995 = torch.operator "onnx.DequantizeLinear"(%2992, %2993, %2994) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %2996 = torch.operator "onnx.Mul"(%2989, %2995) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %2997 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1919> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %2998 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1920> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %2999 = torch.operator "onnx.QuantizeLinear"(%2996, %2997, %2998) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3000 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1921> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3001 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1922> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3002 = torch.operator "onnx.DequantizeLinear"(%2999, %3000, %3001) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3003 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1923> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3004 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1924> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3005 = torch.operator "onnx.QuantizeLinear"(%58, %3003, %3004) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %3006 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1925> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3007 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1926> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3008 = torch.operator "onnx.DequantizeLinear"(%3005, %3006, %3007) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %3009 = torch.operator "onnx.Add"(%3002, %3008) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3010 = torch.operator "onnx.Relu"(%3009) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3011 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1927> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3012 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1928> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3013 = torch.operator "onnx.QuantizeLinear"(%3010, %3011, %3012) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3014 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1929> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3015 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1930> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3016 = torch.operator "onnx.DequantizeLinear"(%3013, %3014, %3015) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3017 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1931> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3018 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1932> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3019 = torch.operator "onnx.QuantizeLinear"(%57, %3017, %3018) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %3020 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1933> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3021 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1934> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3022 = torch.operator "onnx.DequantizeLinear"(%3019, %3020, %3021) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %3023 = torch.operator "onnx.Conv"(%3016, %680, %3022) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3024 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1935> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3025 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1936> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3026 = torch.operator "onnx.QuantizeLinear"(%3023, %3024, %3025) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3027 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1937> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3028 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1938> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3029 = torch.operator "onnx.DequantizeLinear"(%3026, %3027, %3028) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3030 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1939> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3031 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1940> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3032 = torch.operator "onnx.QuantizeLinear"(%56, %3030, %3031) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %3033 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1941> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3034 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1942> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3035 = torch.operator "onnx.DequantizeLinear"(%3032, %3033, %3034) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %3036 = torch.operator "onnx.Mul"(%3029, %3035) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3037 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1943> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3038 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1944> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3039 = torch.operator "onnx.QuantizeLinear"(%3036, %3037, %3038) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3040 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1945> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3041 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1946> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3042 = torch.operator "onnx.DequantizeLinear"(%3039, %3040, %3041) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3043 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1947> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3044 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1948> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3045 = torch.operator "onnx.QuantizeLinear"(%55, %3043, %3044) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %3046 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1949> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3047 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1950> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3048 = torch.operator "onnx.DequantizeLinear"(%3045, %3046, %3047) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %3049 = torch.operator "onnx.Add"(%3042, %3048) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3050 = torch.operator "onnx.Relu"(%3049) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3051 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1951> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3052 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1952> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3053 = torch.operator "onnx.QuantizeLinear"(%3050, %3051, %3052) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3054 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1953> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3055 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1954> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3056 = torch.operator "onnx.DequantizeLinear"(%3053, %3054, %3055) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3057 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1955> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3058 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1956> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3059 = torch.operator "onnx.QuantizeLinear"(%54, %3057, %3058) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %3060 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1957> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3061 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1958> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3062 = torch.operator "onnx.DequantizeLinear"(%3059, %3060, %3061) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %3063 = torch.operator "onnx.Conv"(%3056, %674, %3062) {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<[2,256,?,?],f32>, !torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[1024],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3064 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1959> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3065 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1960> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3066 = torch.operator "onnx.QuantizeLinear"(%3063, %3064, %3065) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3067 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1961> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3068 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1962> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3069 = torch.operator "onnx.DequantizeLinear"(%3066, %3067, %3068) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3070 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1963> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3071 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1964> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3072 = torch.operator "onnx.QuantizeLinear"(%53, %3070, %3071) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %3073 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1965> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3074 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1966> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3075 = torch.operator "onnx.DequantizeLinear"(%3072, %3073, %3074) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %3076 = torch.operator "onnx.Mul"(%3069, %3075) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3077 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1967> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3078 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1968> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3079 = torch.operator "onnx.QuantizeLinear"(%3076, %3077, %3078) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3080 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1969> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3081 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1970> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3082 = torch.operator "onnx.DequantizeLinear"(%3079, %3080, %3081) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3083 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1971> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3084 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1972> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3085 = torch.operator "onnx.QuantizeLinear"(%52, %3083, %3084) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %3086 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1973> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3087 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1974> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3088 = torch.operator "onnx.DequantizeLinear"(%3085, %3086, %3087) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %3089 = torch.operator "onnx.Add"(%3082, %3088) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3090 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1975> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3091 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1976> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3092 = torch.operator "onnx.QuantizeLinear"(%3089, %3090, %3091) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3093 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1977> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3094 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1978> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3095 = torch.operator "onnx.DequantizeLinear"(%3092, %3093, %3094) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3096 = torch.operator "onnx.Add"(%3095, %2976) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3097 = torch.operator "onnx.Relu"(%3096) : (!torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3098 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1979> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3099 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1980> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3100 = torch.operator "onnx.QuantizeLinear"(%3097, %3098, %3099) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3101 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1981> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3102 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1982> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3103 = torch.operator "onnx.DequantizeLinear"(%3100, %3101, %3102) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3104 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1983> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3105 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1984> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3106 = torch.operator "onnx.QuantizeLinear"(%51, %3104, %3105) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %3107 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1985> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3108 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1986> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3109 = torch.operator "onnx.DequantizeLinear"(%3106, %3107, %3108) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %3110 = torch.operator "onnx.Conv"(%3103, %668, %3109) {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<[2,1024,?,?],f32>, !torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3111 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1987> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3112 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1988> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3113 = torch.operator "onnx.QuantizeLinear"(%3110, %3111, %3112) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3114 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1989> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3115 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1990> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3116 = torch.operator "onnx.DequantizeLinear"(%3113, %3114, %3115) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3117 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1991> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3118 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1992> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3119 = torch.operator "onnx.QuantizeLinear"(%50, %3117, %3118) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %3120 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1993> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3121 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1994> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3122 = torch.operator "onnx.DequantizeLinear"(%3119, %3120, %3121) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %3123 = torch.operator "onnx.Mul"(%3116, %3122) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3124 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1995> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3125 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1996> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3126 = torch.operator "onnx.QuantizeLinear"(%3123, %3124, %3125) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3127 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1997> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3128 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1998> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3129 = torch.operator "onnx.DequantizeLinear"(%3126, %3127, %3128) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3130 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__1999> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3131 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2000> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3132 = torch.operator "onnx.QuantizeLinear"(%49, %3130, %3131) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %3133 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2001> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3134 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2002> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3135 = torch.operator "onnx.DequantizeLinear"(%3132, %3133, %3134) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %3136 = torch.operator "onnx.Add"(%3129, %3135) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3137 = torch.operator "onnx.Relu"(%3136) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3138 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2003> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3139 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2004> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3140 = torch.operator "onnx.QuantizeLinear"(%3137, %3138, %3139) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3141 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2005> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3142 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2006> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3143 = torch.operator "onnx.DequantizeLinear"(%3140, %3141, %3142) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3144 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2007> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3145 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2008> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3146 = torch.operator "onnx.QuantizeLinear"(%48, %3144, %3145) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %3147 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2009> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3148 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2010> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3149 = torch.operator "onnx.DequantizeLinear"(%3146, %3147, %3148) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %3150 = torch.operator "onnx.Conv"(%3143, %662, %3149) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3151 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2011> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3152 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2012> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3153 = torch.operator "onnx.QuantizeLinear"(%3150, %3151, %3152) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3154 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2013> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3155 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2014> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3156 = torch.operator "onnx.DequantizeLinear"(%3153, %3154, %3155) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3157 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2015> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3158 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2016> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3159 = torch.operator "onnx.QuantizeLinear"(%47, %3157, %3158) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %3160 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2017> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3161 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2018> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3162 = torch.operator "onnx.DequantizeLinear"(%3159, %3160, %3161) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %3163 = torch.operator "onnx.Mul"(%3156, %3162) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3164 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2019> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3165 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2020> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3166 = torch.operator "onnx.QuantizeLinear"(%3163, %3164, %3165) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3167 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2021> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3168 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2022> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3169 = torch.operator "onnx.DequantizeLinear"(%3166, %3167, %3168) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3170 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2023> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3171 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2024> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3172 = torch.operator "onnx.QuantizeLinear"(%46, %3170, %3171) : (!torch.vtensor<[1,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],si8> 
    %3173 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2025> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3174 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2026> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3175 = torch.operator "onnx.DequantizeLinear"(%3172, %3173, %3174) : (!torch.vtensor<[1,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,256,1,1],f32> 
    %3176 = torch.operator "onnx.Add"(%3169, %3175) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[1,256,1,1],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3177 = torch.operator "onnx.Relu"(%3176) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3178 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2027> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3179 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2028> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3180 = torch.operator "onnx.QuantizeLinear"(%3177, %3178, %3179) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3181 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2029> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3182 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2030> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3183 = torch.operator "onnx.DequantizeLinear"(%3180, %3181, %3182) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3184 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2031> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3185 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2032> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3186 = torch.operator "onnx.QuantizeLinear"(%45, %3184, %3185) : (!torch.vtensor<[1024],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],si8> 
    %3187 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2033> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3188 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2034> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3189 = torch.operator "onnx.DequantizeLinear"(%3186, %3187, %3188) : (!torch.vtensor<[1024],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1024],f32> 
    %3190 = torch.operator "onnx.Conv"(%3183, %656, %3189) {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<[2,256,?,?],f32>, !torch.vtensor<[1024,256,1,1],f32>, !torch.vtensor<[1024],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3191 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2035> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3192 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2036> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3193 = torch.operator "onnx.QuantizeLinear"(%3190, %3191, %3192) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3194 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2037> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3195 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2038> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3196 = torch.operator "onnx.DequantizeLinear"(%3193, %3194, %3195) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3197 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2039> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3198 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2040> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3199 = torch.operator "onnx.QuantizeLinear"(%44, %3197, %3198) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %3200 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2041> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3201 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2042> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3202 = torch.operator "onnx.DequantizeLinear"(%3199, %3200, %3201) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %3203 = torch.operator "onnx.Mul"(%3196, %3202) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3204 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2043> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3205 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2044> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3206 = torch.operator "onnx.QuantizeLinear"(%3203, %3204, %3205) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3207 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2045> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3208 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2046> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3209 = torch.operator "onnx.DequantizeLinear"(%3206, %3207, %3208) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3210 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2047> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3211 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2048> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3212 = torch.operator "onnx.QuantizeLinear"(%43, %3210, %3211) : (!torch.vtensor<[1,1024,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],si8> 
    %3213 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2049> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3214 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2050> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3215 = torch.operator "onnx.DequantizeLinear"(%3212, %3213, %3214) : (!torch.vtensor<[1,1024,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,1024,1,1],f32> 
    %3216 = torch.operator "onnx.Add"(%3209, %3215) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[1,1024,1,1],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3217 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2051> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3218 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2052> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3219 = torch.operator "onnx.QuantizeLinear"(%3216, %3217, %3218) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3220 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2053> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3221 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2054> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3222 = torch.operator "onnx.DequantizeLinear"(%3219, %3220, %3221) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3223 = torch.operator "onnx.Add"(%3222, %3103) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3224 = torch.operator "onnx.Relu"(%3223) : (!torch.vtensor<[2,1024,?,?],f32>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3225 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2055> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3226 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2056> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3227 = torch.operator "onnx.QuantizeLinear"(%3224, %3225, %3226) : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],si8> 
    %3228 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2057> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3229 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2058> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3230 = torch.operator "onnx.DequantizeLinear"(%3227, %3228, %3229) : (!torch.vtensor<[2,1024,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,1024,?,?],f32> 
    %3231 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2059> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3232 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2060> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3233 = torch.operator "onnx.QuantizeLinear"(%42, %3231, %3232) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %3234 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2061> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3235 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2062> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3236 = torch.operator "onnx.DequantizeLinear"(%3233, %3234, %3235) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %3237 = torch.operator "onnx.Conv"(%3230, %650, %3236) {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<[2,1024,?,?],f32>, !torch.vtensor<[512,1024,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3238 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2063> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3239 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2064> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3240 = torch.operator "onnx.QuantizeLinear"(%3237, %3238, %3239) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3241 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2065> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3242 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2066> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3243 = torch.operator "onnx.DequantizeLinear"(%3240, %3241, %3242) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3244 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2067> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3245 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2068> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3246 = torch.operator "onnx.QuantizeLinear"(%41, %3244, %3245) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3247 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2069> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3248 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2070> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3249 = torch.operator "onnx.DequantizeLinear"(%3246, %3247, %3248) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3250 = torch.operator "onnx.Mul"(%3243, %3249) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3251 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2071> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3252 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2072> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3253 = torch.operator "onnx.QuantizeLinear"(%3250, %3251, %3252) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3254 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2073> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3255 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2074> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3256 = torch.operator "onnx.DequantizeLinear"(%3253, %3254, %3255) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3257 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2075> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3258 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2076> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3259 = torch.operator "onnx.QuantizeLinear"(%40, %3257, %3258) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3260 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2077> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3261 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2078> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3262 = torch.operator "onnx.DequantizeLinear"(%3259, %3260, %3261) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3263 = torch.operator "onnx.Add"(%3256, %3262) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3264 = torch.operator "onnx.Relu"(%3263) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3265 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2079> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3266 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2080> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3267 = torch.operator "onnx.QuantizeLinear"(%3264, %3265, %3266) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3268 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2081> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3269 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2082> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3270 = torch.operator "onnx.DequantizeLinear"(%3267, %3268, %3269) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3271 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2083> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3272 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2084> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3273 = torch.operator "onnx.QuantizeLinear"(%39, %3271, %3272) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %3274 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2085> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3275 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2086> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3276 = torch.operator "onnx.DequantizeLinear"(%3273, %3274, %3275) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %3277 = torch.operator "onnx.Conv"(%3270, %644, %3276) {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 = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3278 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2087> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3279 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2088> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3280 = torch.operator "onnx.QuantizeLinear"(%3277, %3278, %3279) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3281 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2089> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3282 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2090> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3283 = torch.operator "onnx.DequantizeLinear"(%3280, %3281, %3282) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3284 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2091> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3285 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2092> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3286 = torch.operator "onnx.QuantizeLinear"(%38, %3284, %3285) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3287 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2093> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3288 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2094> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3289 = torch.operator "onnx.DequantizeLinear"(%3286, %3287, %3288) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3290 = torch.operator "onnx.Mul"(%3283, %3289) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3291 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2095> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3292 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2096> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3293 = torch.operator "onnx.QuantizeLinear"(%3290, %3291, %3292) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3294 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2097> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3295 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2098> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3296 = torch.operator "onnx.DequantizeLinear"(%3293, %3294, %3295) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3297 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2099> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3298 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2100> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3299 = torch.operator "onnx.QuantizeLinear"(%37, %3297, %3298) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3300 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2101> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3301 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2102> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3302 = torch.operator "onnx.DequantizeLinear"(%3299, %3300, %3301) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3303 = torch.operator "onnx.Add"(%3296, %3302) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3304 = torch.operator "onnx.Relu"(%3303) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3305 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2103> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3306 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2104> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3307 = torch.operator "onnx.QuantizeLinear"(%3304, %3305, %3306) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3308 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2105> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3309 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2106> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3310 = torch.operator "onnx.DequantizeLinear"(%3307, %3308, %3309) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3311 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2107> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3312 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2108> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3313 = torch.operator "onnx.QuantizeLinear"(%36, %3311, %3312) : (!torch.vtensor<[2048],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],si8> 
    %3314 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2109> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3315 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2110> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3316 = torch.operator "onnx.DequantizeLinear"(%3313, %3314, %3315) : (!torch.vtensor<[2048],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],f32> 
    %3317 = torch.operator "onnx.Conv"(%3310, %638, %3316) {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<[2,512,?,?],f32>, !torch.vtensor<[2048,512,1,1],f32>, !torch.vtensor<[2048],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3318 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2111> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3319 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2112> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3320 = torch.operator "onnx.QuantizeLinear"(%3317, %3318, %3319) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3321 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2113> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3322 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2114> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3323 = torch.operator "onnx.DequantizeLinear"(%3320, %3321, %3322) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3324 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2115> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3325 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2116> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3326 = torch.operator "onnx.QuantizeLinear"(%35, %3324, %3325) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3327 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2117> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3328 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2118> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3329 = torch.operator "onnx.DequantizeLinear"(%3326, %3327, %3328) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3330 = torch.operator "onnx.Mul"(%3323, %3329) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3331 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2119> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3332 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2120> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3333 = torch.operator "onnx.QuantizeLinear"(%3330, %3331, %3332) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3334 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2121> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3335 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2122> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3336 = torch.operator "onnx.DequantizeLinear"(%3333, %3334, %3335) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3337 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2123> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3338 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2124> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3339 = torch.operator "onnx.QuantizeLinear"(%34, %3337, %3338) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3340 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2125> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3341 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2126> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3342 = torch.operator "onnx.DequantizeLinear"(%3339, %3340, %3341) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3343 = torch.operator "onnx.Add"(%3336, %3342) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3344 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2127> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3345 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2128> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3346 = torch.operator "onnx.QuantizeLinear"(%3343, %3344, %3345) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3347 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2129> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3348 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2130> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3349 = torch.operator "onnx.DequantizeLinear"(%3346, %3347, %3348) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3350 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2131> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3351 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2132> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3352 = torch.operator "onnx.QuantizeLinear"(%33, %3350, %3351) : (!torch.vtensor<[2048],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],si8> 
    %3353 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2133> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3354 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2134> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3355 = torch.operator "onnx.DequantizeLinear"(%3352, %3353, %3354) : (!torch.vtensor<[2048],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],f32> 
    %3356 = torch.operator "onnx.Conv"(%3230, %632, %3355) {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 = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,1024,?,?],f32>, !torch.vtensor<[2048,1024,1,1],f32>, !torch.vtensor<[2048],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3357 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2135> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3358 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2136> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3359 = torch.operator "onnx.QuantizeLinear"(%3356, %3357, %3358) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3360 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2137> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3361 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2138> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3362 = torch.operator "onnx.DequantizeLinear"(%3359, %3360, %3361) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3363 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2139> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3364 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2140> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3365 = torch.operator "onnx.QuantizeLinear"(%32, %3363, %3364) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3366 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2141> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3367 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2142> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3368 = torch.operator "onnx.DequantizeLinear"(%3365, %3366, %3367) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3369 = torch.operator "onnx.Mul"(%3362, %3368) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3370 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2143> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3371 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2144> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3372 = torch.operator "onnx.QuantizeLinear"(%3369, %3370, %3371) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3373 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2145> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3374 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2146> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3375 = torch.operator "onnx.DequantizeLinear"(%3372, %3373, %3374) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3376 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2147> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3377 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2148> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3378 = torch.operator "onnx.QuantizeLinear"(%31, %3376, %3377) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3379 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2149> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3380 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2150> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3381 = torch.operator "onnx.DequantizeLinear"(%3378, %3379, %3380) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3382 = torch.operator "onnx.Add"(%3375, %3381) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3383 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2151> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3384 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2152> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3385 = torch.operator "onnx.QuantizeLinear"(%3382, %3383, %3384) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3386 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2153> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3387 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2154> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3388 = torch.operator "onnx.DequantizeLinear"(%3385, %3386, %3387) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3389 = torch.operator "onnx.Add"(%3349, %3388) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[2,2048,?,?],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3390 = torch.operator "onnx.Relu"(%3389) : (!torch.vtensor<[2,2048,?,?],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3391 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2155> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3392 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2156> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3393 = torch.operator "onnx.QuantizeLinear"(%3390, %3391, %3392) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3394 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2157> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3395 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2158> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3396 = torch.operator "onnx.DequantizeLinear"(%3393, %3394, %3395) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3397 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2159> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3398 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2160> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3399 = torch.operator "onnx.QuantizeLinear"(%30, %3397, %3398) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %3400 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2161> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3401 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2162> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3402 = torch.operator "onnx.DequantizeLinear"(%3399, %3400, %3401) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %3403 = torch.operator "onnx.Conv"(%3396, %626, %3402) {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<[2,2048,?,?],f32>, !torch.vtensor<[512,2048,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3404 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2163> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3405 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2164> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3406 = torch.operator "onnx.QuantizeLinear"(%3403, %3404, %3405) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3407 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2165> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3408 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2166> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3409 = torch.operator "onnx.DequantizeLinear"(%3406, %3407, %3408) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3410 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2167> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3411 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2168> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3412 = torch.operator "onnx.QuantizeLinear"(%29, %3410, %3411) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3413 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2169> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3414 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2170> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3415 = torch.operator "onnx.DequantizeLinear"(%3412, %3413, %3414) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3416 = torch.operator "onnx.Mul"(%3409, %3415) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3417 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2171> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3418 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2172> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3419 = torch.operator "onnx.QuantizeLinear"(%3416, %3417, %3418) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3420 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2173> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3421 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2174> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3422 = torch.operator "onnx.DequantizeLinear"(%3419, %3420, %3421) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3423 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2175> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3424 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2176> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3425 = torch.operator "onnx.QuantizeLinear"(%28, %3423, %3424) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3426 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2177> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3427 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2178> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3428 = torch.operator "onnx.DequantizeLinear"(%3425, %3426, %3427) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3429 = torch.operator "onnx.Add"(%3422, %3428) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3430 = torch.operator "onnx.Relu"(%3429) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3431 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2179> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3432 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2180> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3433 = torch.operator "onnx.QuantizeLinear"(%3430, %3431, %3432) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3434 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2181> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3435 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2182> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3436 = torch.operator "onnx.DequantizeLinear"(%3433, %3434, %3435) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3437 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2183> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3438 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2184> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3439 = torch.operator "onnx.QuantizeLinear"(%27, %3437, %3438) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %3440 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2185> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3441 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2186> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3442 = torch.operator "onnx.DequantizeLinear"(%3439, %3440, %3441) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %3443 = torch.operator "onnx.Conv"(%3436, %620, %3442) {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<[2,512,?,?],f32>, !torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3444 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2187> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3445 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2188> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3446 = torch.operator "onnx.QuantizeLinear"(%3443, %3444, %3445) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3447 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2189> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3448 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2190> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3449 = torch.operator "onnx.DequantizeLinear"(%3446, %3447, %3448) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3450 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2191> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3451 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2192> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3452 = torch.operator "onnx.QuantizeLinear"(%26, %3450, %3451) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3453 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2193> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3454 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2194> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3455 = torch.operator "onnx.DequantizeLinear"(%3452, %3453, %3454) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3456 = torch.operator "onnx.Mul"(%3449, %3455) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3457 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2195> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3458 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2196> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3459 = torch.operator "onnx.QuantizeLinear"(%3456, %3457, %3458) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3460 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2197> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3461 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2198> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3462 = torch.operator "onnx.DequantizeLinear"(%3459, %3460, %3461) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3463 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2199> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3464 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2200> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3465 = torch.operator "onnx.QuantizeLinear"(%25, %3463, %3464) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3466 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2201> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3467 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2202> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3468 = torch.operator "onnx.DequantizeLinear"(%3465, %3466, %3467) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3469 = torch.operator "onnx.Add"(%3462, %3468) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3470 = torch.operator "onnx.Relu"(%3469) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3471 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2203> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3472 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2204> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3473 = torch.operator "onnx.QuantizeLinear"(%3470, %3471, %3472) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3474 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2205> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3475 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2206> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3476 = torch.operator "onnx.DequantizeLinear"(%3473, %3474, %3475) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3477 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2207> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3478 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2208> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3479 = torch.operator "onnx.QuantizeLinear"(%24, %3477, %3478) : (!torch.vtensor<[2048],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],si8> 
    %3480 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2209> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3481 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2210> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3482 = torch.operator "onnx.DequantizeLinear"(%3479, %3480, %3481) : (!torch.vtensor<[2048],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],f32> 
    %3483 = torch.operator "onnx.Conv"(%3476, %614, %3482) {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<[2,512,?,?],f32>, !torch.vtensor<[2048,512,1,1],f32>, !torch.vtensor<[2048],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3484 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2211> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3485 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2212> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3486 = torch.operator "onnx.QuantizeLinear"(%3483, %3484, %3485) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3487 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2213> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3488 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2214> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3489 = torch.operator "onnx.DequantizeLinear"(%3486, %3487, %3488) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3490 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2215> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3491 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2216> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3492 = torch.operator "onnx.QuantizeLinear"(%23, %3490, %3491) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3493 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2217> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3494 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2218> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3495 = torch.operator "onnx.DequantizeLinear"(%3492, %3493, %3494) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3496 = torch.operator "onnx.Mul"(%3489, %3495) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3497 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2219> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3498 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2220> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3499 = torch.operator "onnx.QuantizeLinear"(%3496, %3497, %3498) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3500 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2221> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3501 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2222> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3502 = torch.operator "onnx.DequantizeLinear"(%3499, %3500, %3501) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3503 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2223> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3504 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2224> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3505 = torch.operator "onnx.QuantizeLinear"(%22, %3503, %3504) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3506 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2225> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3507 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2226> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3508 = torch.operator "onnx.DequantizeLinear"(%3505, %3506, %3507) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3509 = torch.operator "onnx.Add"(%3502, %3508) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3510 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2227> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3511 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2228> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3512 = torch.operator "onnx.QuantizeLinear"(%3509, %3510, %3511) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3513 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2229> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3514 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2230> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3515 = torch.operator "onnx.DequantizeLinear"(%3512, %3513, %3514) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3516 = torch.operator "onnx.Add"(%3515, %3396) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[2,2048,?,?],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3517 = torch.operator "onnx.Relu"(%3516) : (!torch.vtensor<[2,2048,?,?],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3518 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2231> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3519 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2232> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3520 = torch.operator "onnx.QuantizeLinear"(%3517, %3518, %3519) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3521 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2233> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3522 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2234> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3523 = torch.operator "onnx.DequantizeLinear"(%3520, %3521, %3522) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3524 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2235> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3525 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2236> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3526 = torch.operator "onnx.QuantizeLinear"(%21, %3524, %3525) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %3527 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2237> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3528 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2238> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3529 = torch.operator "onnx.DequantizeLinear"(%3526, %3527, %3528) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %3530 = torch.operator "onnx.Conv"(%3523, %608, %3529) {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<[2,2048,?,?],f32>, !torch.vtensor<[512,2048,1,1],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3531 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2239> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3532 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2240> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3533 = torch.operator "onnx.QuantizeLinear"(%3530, %3531, %3532) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3534 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2241> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3535 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2242> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3536 = torch.operator "onnx.DequantizeLinear"(%3533, %3534, %3535) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3537 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2243> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3538 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2244> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3539 = torch.operator "onnx.QuantizeLinear"(%20, %3537, %3538) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3540 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2245> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3541 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2246> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3542 = torch.operator "onnx.DequantizeLinear"(%3539, %3540, %3541) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3543 = torch.operator "onnx.Mul"(%3536, %3542) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3544 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2247> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3545 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2248> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3546 = torch.operator "onnx.QuantizeLinear"(%3543, %3544, %3545) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3547 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2249> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3548 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2250> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3549 = torch.operator "onnx.DequantizeLinear"(%3546, %3547, %3548) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3550 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2251> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3551 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2252> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3552 = torch.operator "onnx.QuantizeLinear"(%19, %3550, %3551) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3553 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2253> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3554 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2254> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3555 = torch.operator "onnx.DequantizeLinear"(%3552, %3553, %3554) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3556 = torch.operator "onnx.Add"(%3549, %3555) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3557 = torch.operator "onnx.Relu"(%3556) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3558 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2255> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3559 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2256> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3560 = torch.operator "onnx.QuantizeLinear"(%3557, %3558, %3559) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3561 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2257> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3562 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2258> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3563 = torch.operator "onnx.DequantizeLinear"(%3560, %3561, %3562) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3564 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2259> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3565 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2260> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3566 = torch.operator "onnx.QuantizeLinear"(%18, %3564, %3565) : (!torch.vtensor<[512],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],si8> 
    %3567 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2261> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3568 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2262> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3569 = torch.operator "onnx.DequantizeLinear"(%3566, %3567, %3568) : (!torch.vtensor<[512],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[512],f32> 
    %3570 = torch.operator "onnx.Conv"(%3563, %602, %3569) {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<[2,512,?,?],f32>, !torch.vtensor<[512,512,3,3],f32>, !torch.vtensor<[512],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3571 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2263> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3572 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2264> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3573 = torch.operator "onnx.QuantizeLinear"(%3570, %3571, %3572) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3574 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2265> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3575 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2266> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3576 = torch.operator "onnx.DequantizeLinear"(%3573, %3574, %3575) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3577 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2267> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3578 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2268> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3579 = torch.operator "onnx.QuantizeLinear"(%17, %3577, %3578) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3580 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2269> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3581 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2270> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3582 = torch.operator "onnx.DequantizeLinear"(%3579, %3580, %3581) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3583 = torch.operator "onnx.Mul"(%3576, %3582) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3584 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2271> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3585 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2272> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3586 = torch.operator "onnx.QuantizeLinear"(%3583, %3584, %3585) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3587 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2273> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3588 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2274> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3589 = torch.operator "onnx.DequantizeLinear"(%3586, %3587, %3588) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3590 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2275> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3591 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2276> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3592 = torch.operator "onnx.QuantizeLinear"(%16, %3590, %3591) : (!torch.vtensor<[1,512,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],si8> 
    %3593 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2277> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3594 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2278> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3595 = torch.operator "onnx.DequantizeLinear"(%3592, %3593, %3594) : (!torch.vtensor<[1,512,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,512,1,1],f32> 
    %3596 = torch.operator "onnx.Add"(%3589, %3595) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[1,512,1,1],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3597 = torch.operator "onnx.Relu"(%3596) : (!torch.vtensor<[2,512,?,?],f32>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3598 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2279> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3599 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2280> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3600 = torch.operator "onnx.QuantizeLinear"(%3597, %3598, %3599) : (!torch.vtensor<[2,512,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],si8> 
    %3601 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2281> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3602 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2282> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3603 = torch.operator "onnx.DequantizeLinear"(%3600, %3601, %3602) : (!torch.vtensor<[2,512,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,512,?,?],f32> 
    %3604 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2283> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3605 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2284> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3606 = torch.operator "onnx.QuantizeLinear"(%15, %3604, %3605) : (!torch.vtensor<[2048],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],si8> 
    %3607 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2285> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3608 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2286> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3609 = torch.operator "onnx.DequantizeLinear"(%3606, %3607, %3608) : (!torch.vtensor<[2048],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2048],f32> 
    %3610 = torch.operator "onnx.Conv"(%3603, %596, %3609) {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<[2,512,?,?],f32>, !torch.vtensor<[2048,512,1,1],f32>, !torch.vtensor<[2048],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3611 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2287> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3612 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2288> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3613 = torch.operator "onnx.QuantizeLinear"(%3610, %3611, %3612) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3614 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2289> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3615 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2290> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3616 = torch.operator "onnx.DequantizeLinear"(%3613, %3614, %3615) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3617 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2291> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3618 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2292> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3619 = torch.operator "onnx.QuantizeLinear"(%14, %3617, %3618) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3620 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2293> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3621 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2294> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3622 = torch.operator "onnx.DequantizeLinear"(%3619, %3620, %3621) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3623 = torch.operator "onnx.Mul"(%3616, %3622) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3624 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2295> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3625 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2296> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3626 = torch.operator "onnx.QuantizeLinear"(%3623, %3624, %3625) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2297> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3628 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2298> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3629 = torch.operator "onnx.DequantizeLinear"(%3626, %3627, %3628) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3630 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2299> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3631 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2300> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3632 = torch.operator "onnx.QuantizeLinear"(%13, %3630, %3631) : (!torch.vtensor<[1,2048,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],si8> 
    %3633 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2301> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3634 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2302> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3635 = torch.operator "onnx.DequantizeLinear"(%3632, %3633, %3634) : (!torch.vtensor<[1,2048,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,2048,1,1],f32> 
    %3636 = torch.operator "onnx.Add"(%3629, %3635) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[1,2048,1,1],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3637 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2303> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3638 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2304> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3639 = torch.operator "onnx.QuantizeLinear"(%3636, %3637, %3638) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3640 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2305> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3641 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2306> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3642 = torch.operator "onnx.DequantizeLinear"(%3639, %3640, %3641) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3643 = torch.operator "onnx.Add"(%3642, %3523) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[2,2048,?,?],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3644 = torch.operator "onnx.Relu"(%3643) : (!torch.vtensor<[2,2048,?,?],f32>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3645 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2307> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3646 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2308> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3647 = torch.operator "onnx.QuantizeLinear"(%3644, %3645, %3646) : (!torch.vtensor<[2,2048,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],si8> 
    %3648 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2309> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3649 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2310> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3650 = torch.operator "onnx.DequantizeLinear"(%3647, %3648, %3649) : (!torch.vtensor<[2,2048,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,2048,?,?],f32> 
    %3651 = torch.operator "onnx.Conv"(%3650, %584, %590) {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<[2,2048,?,?],f32>, !torch.vtensor<[256,2048,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2311> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3653 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2312> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3654 = torch.operator "onnx.QuantizeLinear"(%3651, %3652, %3653) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3655 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2313> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3656 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2314> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3657 = torch.operator "onnx.DequantizeLinear"(%3654, %3655, %3656) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3658 = torch.operator "onnx.Conv"(%3657, %572, %578) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3659 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2315> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3660 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2316> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3661 = torch.operator "onnx.QuantizeLinear"(%3658, %3659, %3660) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3662 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2317> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3663 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2318> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3664 = torch.operator "onnx.DequantizeLinear"(%3661, %3662, %3663) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3665 = torch.operator "onnx.Conv"(%3230, %560, %566) {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<[2,1024,?,?],f32>, !torch.vtensor<[256,1024,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2319> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3667 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2320> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3668 = torch.operator "onnx.QuantizeLinear"(%3665, %3666, %3667) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3669 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2321> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3670 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2322> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3671 = torch.operator "onnx.DequantizeLinear"(%3668, %3669, %3670) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3672 = torch.operator "onnx.Shape"(%3671) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3673 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2323> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %3674 = torch.operator "onnx.Gather"(%3672, %3673) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %3675 = torch.operator "onnx.Shape"(%3671) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3676 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2324> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %3677 = torch.operator "onnx.Gather"(%3675, %3676) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %3678 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2325> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3679 = torch.operator "onnx.Unsqueeze"(%3674, %3678) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %3680 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2326> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3681 = torch.operator "onnx.Unsqueeze"(%3677, %3680) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %3682 = torch.operator "onnx.Concat"(%3679, %3681) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %3683 = torch.operator "onnx.Shape"(%3657) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3684 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2327> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3685 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2328> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3686 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2329> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3687 = torch.operator "onnx.Slice"(%3683, %3685, %3686, %3684) : (!torch.vtensor<[4],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %3688 = torch.operator "onnx.Cast"(%3682) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],si64> 
    %3689 = torch.operator "onnx.Concat"(%3687, %3688) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4],si64> 
    %3690 = torch.operator "onnx.Resize"(%3657, %none, %none, %3689) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[2,256,?,?],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3691 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2330> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3692 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2331> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3693 = torch.operator "onnx.QuantizeLinear"(%3690, %3691, %3692) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3694 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2332> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3695 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2333> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3696 = torch.operator "onnx.DequantizeLinear"(%3693, %3694, %3695) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3697 = torch.operator "onnx.Add"(%3671, %3696) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3698 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2334> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3699 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2335> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3700 = torch.operator "onnx.QuantizeLinear"(%3697, %3698, %3699) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3701 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2336> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3702 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2337> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3703 = torch.operator "onnx.DequantizeLinear"(%3700, %3701, %3702) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3704 = torch.operator "onnx.Conv"(%3703, %548, %554) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3705 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2338> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3706 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2339> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3707 = torch.operator "onnx.QuantizeLinear"(%3704, %3705, %3706) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3708 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2340> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3709 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2341> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3710 = torch.operator "onnx.DequantizeLinear"(%3707, %3708, %3709) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3711 = torch.operator "onnx.Conv"(%2429, %536, %542) {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<[2,512,?,?],f32>, !torch.vtensor<[256,512,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3712 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2342> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3713 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2343> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3714 = torch.operator "onnx.QuantizeLinear"(%3711, %3712, %3713) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3715 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2344> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3716 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2345> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3717 = torch.operator "onnx.DequantizeLinear"(%3714, %3715, %3716) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3718 = torch.operator "onnx.Shape"(%3717) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3719 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2346> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %3720 = torch.operator "onnx.Gather"(%3718, %3719) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %3721 = torch.operator "onnx.Shape"(%3717) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3722 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2347> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %3723 = torch.operator "onnx.Gather"(%3721, %3722) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %3724 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2348> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3725 = torch.operator "onnx.Unsqueeze"(%3720, %3724) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %3726 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2349> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3727 = torch.operator "onnx.Unsqueeze"(%3723, %3726) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %3728 = torch.operator "onnx.Concat"(%3725, %3727) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %3729 = torch.operator "onnx.Shape"(%3703) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3730 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2350> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3731 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2351> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3732 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2352> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3733 = torch.operator "onnx.Slice"(%3729, %3731, %3732, %3730) : (!torch.vtensor<[4],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %3734 = torch.operator "onnx.Cast"(%3728) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],si64> 
    %3735 = torch.operator "onnx.Concat"(%3733, %3734) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4],si64> 
    %3736 = torch.operator "onnx.Resize"(%3703, %none, %none, %3735) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[2,256,?,?],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3737 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2353> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3738 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2354> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3739 = torch.operator "onnx.QuantizeLinear"(%3736, %3737, %3738) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3740 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2355> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3741 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2356> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3742 = torch.operator "onnx.DequantizeLinear"(%3739, %3740, %3741) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3743 = torch.operator "onnx.Add"(%3717, %3742) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3744 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2357> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3745 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2358> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3746 = torch.operator "onnx.QuantizeLinear"(%3743, %3744, %3745) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3747 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2359> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3748 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2360> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3749 = torch.operator "onnx.DequantizeLinear"(%3746, %3747, %3748) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3750 = torch.operator "onnx.Conv"(%3749, %524, %530) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3751 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2361> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3752 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2362> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3753 = torch.operator "onnx.QuantizeLinear"(%3750, %3751, %3752) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3754 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2363> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3755 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2364> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3756 = torch.operator "onnx.DequantizeLinear"(%3753, %3754, %3755) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3757 = torch.operator "onnx.Conv"(%1882, %512, %518) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,1,1],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3758 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2365> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3759 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2366> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3760 = torch.operator "onnx.QuantizeLinear"(%3757, %3758, %3759) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3761 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2367> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3762 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2368> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3763 = torch.operator "onnx.DequantizeLinear"(%3760, %3761, %3762) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3764 = torch.operator "onnx.Shape"(%3763) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3765 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2369> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %3766 = torch.operator "onnx.Gather"(%3764, %3765) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %3767 = torch.operator "onnx.Shape"(%3763) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3768 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2370> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %3769 = torch.operator "onnx.Gather"(%3767, %3768) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %3770 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2371> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3771 = torch.operator "onnx.Unsqueeze"(%3766, %3770) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %3772 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2372> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3773 = torch.operator "onnx.Unsqueeze"(%3769, %3772) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %3774 = torch.operator "onnx.Concat"(%3771, %3773) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %3775 = torch.operator "onnx.Shape"(%3749) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %3776 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2373> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3777 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2374> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3778 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2375> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %3779 = torch.operator "onnx.Slice"(%3775, %3777, %3778, %3776) : (!torch.vtensor<[4],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %3780 = torch.operator "onnx.Cast"(%3774) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],si64> 
    %3781 = torch.operator "onnx.Concat"(%3779, %3780) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[4],si64> 
    %3782 = torch.operator "onnx.Resize"(%3749, %none, %none, %3781) {torch.onnx.coordinate_transformation_mode = "asymmetric", torch.onnx.cubic_coeff_a = -7.500000e-01 : f32, torch.onnx.mode = "nearest", torch.onnx.nearest_mode = "floor"} : (!torch.vtensor<[2,256,?,?],f32>, !torch.none, !torch.none, !torch.vtensor<[4],si64>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3783 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2376> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3784 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2377> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3785 = torch.operator "onnx.QuantizeLinear"(%3782, %3783, %3784) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3786 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2378> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3787 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2379> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3788 = torch.operator "onnx.DequantizeLinear"(%3785, %3786, %3787) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3789 = torch.operator "onnx.Add"(%3763, %3788) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3790 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2380> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3791 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2381> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3792 = torch.operator "onnx.QuantizeLinear"(%3789, %3790, %3791) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3793 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2382> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3794 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2383> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3795 = torch.operator "onnx.DequantizeLinear"(%3792, %3793, %3794) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3796 = torch.operator "onnx.Conv"(%3795, %500, %506) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3797 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2384> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3798 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2385> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3799 = torch.operator "onnx.QuantizeLinear"(%3796, %3797, %3798) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3800 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2386> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3801 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2387> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3802 = torch.operator "onnx.DequantizeLinear"(%3799, %3800, %3801) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3803 = torch.operator "onnx.MaxPool"(%3664) {torch.onnx.ceil_mode = 0 : si64, torch.onnx.kernel_shape = [1 : si64, 1 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3804 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2388> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3805 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2389> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3806 = torch.operator "onnx.QuantizeLinear"(%3803, %3804, %3805) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3807 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2390> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3808 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2391> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3809 = torch.operator "onnx.DequantizeLinear"(%3806, %3807, %3808) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3810 = torch.operator "onnx.Conv"(%3802, %488, %494) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3811 = torch.operator "onnx.Relu"(%3810) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3812 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2392> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3813 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2393> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3814 = torch.operator "onnx.QuantizeLinear"(%3811, %3812, %3813) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3815 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2394> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3816 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2395> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3817 = torch.operator "onnx.DequantizeLinear"(%3814, %3815, %3816) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3818 = torch.operator "onnx.Conv"(%3817, %476, %482) {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<[2,256,?,?],f32>, !torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[3],f32>) -> !torch.vtensor<[2,3,?,?],f32> 
    %3819 = torch.operator "onnx.Conv"(%3817, %464, %470) {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<[2,256,?,?],f32>, !torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[12],f32>) -> !torch.vtensor<[2,12,?,?],f32> 
    %3820 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2396> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %3821 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2397> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3822 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2398> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3823 = torch.operator "onnx.QuantizeLinear"(%3820, %3821, %3822) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %3824 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2399> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3825 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2400> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3826 = torch.operator "onnx.DequantizeLinear"(%3823, %3824, %3825) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %3827 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2401> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3828 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2402> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3829 = torch.operator "onnx.QuantizeLinear"(%12, %3827, %3828) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %3830 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2403> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3831 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2404> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3832 = torch.operator "onnx.DequantizeLinear"(%3829, %3830, %3831) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %3833 = torch.operator "onnx.Conv"(%3756, %3826, %3832) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3834 = torch.operator "onnx.Relu"(%3833) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3835 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2405> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3836 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2406> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3837 = torch.operator "onnx.QuantizeLinear"(%3834, %3835, %3836) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3838 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2407> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3839 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2408> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3840 = torch.operator "onnx.DequantizeLinear"(%3837, %3838, %3839) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3841 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2409> : tensor<3x256x1x1xf32>} : () -> !torch.vtensor<[3,256,1,1],f32> 
    %3842 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2410> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3843 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2411> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3844 = torch.operator "onnx.QuantizeLinear"(%3841, %3842, %3843) : (!torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],si8> 
    %3845 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2412> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3846 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2413> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3847 = torch.operator "onnx.DequantizeLinear"(%3844, %3845, %3846) : (!torch.vtensor<[3,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],f32> 
    %3848 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2414> : tensor<3xf32>} : () -> !torch.vtensor<[3],f32> 
    %3849 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2415> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3850 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2416> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3851 = torch.operator "onnx.QuantizeLinear"(%3848, %3849, %3850) : (!torch.vtensor<[3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],si8> 
    %3852 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2417> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3853 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2418> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3854 = torch.operator "onnx.DequantizeLinear"(%3851, %3852, %3853) : (!torch.vtensor<[3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],f32> 
    %3855 = torch.operator "onnx.Conv"(%3840, %3847, %3854) {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<[2,256,?,?],f32>, !torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[3],f32>) -> !torch.vtensor<[2,3,?,?],f32> 
    %3856 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2419> : tensor<12x256x1x1xf32>} : () -> !torch.vtensor<[12,256,1,1],f32> 
    %3857 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2420> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3858 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2421> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3859 = torch.operator "onnx.QuantizeLinear"(%3856, %3857, %3858) : (!torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],si8> 
    %3860 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2422> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3861 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2423> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3862 = torch.operator "onnx.DequantizeLinear"(%3859, %3860, %3861) : (!torch.vtensor<[12,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],f32> 
    %3863 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2424> : tensor<12xf32>} : () -> !torch.vtensor<[12],f32> 
    %3864 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2425> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3865 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2426> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3866 = torch.operator "onnx.QuantizeLinear"(%3863, %3864, %3865) : (!torch.vtensor<[12],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],si8> 
    %3867 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2427> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3868 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2428> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3869 = torch.operator "onnx.DequantizeLinear"(%3866, %3867, %3868) : (!torch.vtensor<[12],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],f32> 
    %3870 = torch.operator "onnx.Conv"(%3840, %3862, %3869) {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<[2,256,?,?],f32>, !torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[12],f32>) -> !torch.vtensor<[2,12,?,?],f32> 
    %3871 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2429> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %3872 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2430> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3873 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2431> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3874 = torch.operator "onnx.QuantizeLinear"(%3871, %3872, %3873) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %3875 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2432> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3876 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2433> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3877 = torch.operator "onnx.DequantizeLinear"(%3874, %3875, %3876) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %3878 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2434> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3879 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2435> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3880 = torch.operator "onnx.QuantizeLinear"(%11, %3878, %3879) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %3881 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2436> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3882 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2437> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3883 = torch.operator "onnx.DequantizeLinear"(%3880, %3881, %3882) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %3884 = torch.operator "onnx.Conv"(%3710, %3877, %3883) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3885 = torch.operator "onnx.Relu"(%3884) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3886 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2438> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3887 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2439> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3888 = torch.operator "onnx.QuantizeLinear"(%3885, %3886, %3887) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3889 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2440> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3890 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2441> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3891 = torch.operator "onnx.DequantizeLinear"(%3888, %3889, %3890) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3892 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2442> : tensor<3x256x1x1xf32>} : () -> !torch.vtensor<[3,256,1,1],f32> 
    %3893 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2443> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3894 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2444> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3895 = torch.operator "onnx.QuantizeLinear"(%3892, %3893, %3894) : (!torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],si8> 
    %3896 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2445> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3897 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2446> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3898 = torch.operator "onnx.DequantizeLinear"(%3895, %3896, %3897) : (!torch.vtensor<[3,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],f32> 
    %3899 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2447> : tensor<3xf32>} : () -> !torch.vtensor<[3],f32> 
    %3900 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2448> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3901 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2449> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3902 = torch.operator "onnx.QuantizeLinear"(%3899, %3900, %3901) : (!torch.vtensor<[3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],si8> 
    %3903 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2450> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3904 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2451> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3905 = torch.operator "onnx.DequantizeLinear"(%3902, %3903, %3904) : (!torch.vtensor<[3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],f32> 
    %3906 = torch.operator "onnx.Conv"(%3891, %3898, %3905) {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<[2,256,?,?],f32>, !torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[3],f32>) -> !torch.vtensor<[2,3,?,?],f32> 
    %3907 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2452> : tensor<12x256x1x1xf32>} : () -> !torch.vtensor<[12,256,1,1],f32> 
    %3908 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2453> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3909 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2454> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3910 = torch.operator "onnx.QuantizeLinear"(%3907, %3908, %3909) : (!torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],si8> 
    %3911 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2455> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3912 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2456> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3913 = torch.operator "onnx.DequantizeLinear"(%3910, %3911, %3912) : (!torch.vtensor<[12,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],f32> 
    %3914 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2457> : tensor<12xf32>} : () -> !torch.vtensor<[12],f32> 
    %3915 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2458> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3916 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2459> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3917 = torch.operator "onnx.QuantizeLinear"(%3914, %3915, %3916) : (!torch.vtensor<[12],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],si8> 
    %3918 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2460> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3919 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2461> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3920 = torch.operator "onnx.DequantizeLinear"(%3917, %3918, %3919) : (!torch.vtensor<[12],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],f32> 
    %3921 = torch.operator "onnx.Conv"(%3891, %3913, %3920) {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<[2,256,?,?],f32>, !torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[12],f32>) -> !torch.vtensor<[2,12,?,?],f32> 
    %3922 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2462> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %3923 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2463> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3924 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2464> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3925 = torch.operator "onnx.QuantizeLinear"(%3922, %3923, %3924) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %3926 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2465> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3927 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2466> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3928 = torch.operator "onnx.DequantizeLinear"(%3925, %3926, %3927) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %3929 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2467> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3930 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2468> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3931 = torch.operator "onnx.QuantizeLinear"(%10, %3929, %3930) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %3932 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2469> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3933 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2470> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3934 = torch.operator "onnx.DequantizeLinear"(%3931, %3932, %3933) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %3935 = torch.operator "onnx.Conv"(%3664, %3928, %3934) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3936 = torch.operator "onnx.Relu"(%3935) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3937 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2471> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3938 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2472> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3939 = torch.operator "onnx.QuantizeLinear"(%3936, %3937, %3938) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3940 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2473> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3941 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2474> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3942 = torch.operator "onnx.DequantizeLinear"(%3939, %3940, %3941) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3943 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2475> : tensor<3x256x1x1xf32>} : () -> !torch.vtensor<[3,256,1,1],f32> 
    %3944 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2476> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3945 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2477> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3946 = torch.operator "onnx.QuantizeLinear"(%3943, %3944, %3945) : (!torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],si8> 
    %3947 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2478> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3948 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2479> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3949 = torch.operator "onnx.DequantizeLinear"(%3946, %3947, %3948) : (!torch.vtensor<[3,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],f32> 
    %3950 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2480> : tensor<3xf32>} : () -> !torch.vtensor<[3],f32> 
    %3951 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2481> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3952 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2482> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3953 = torch.operator "onnx.QuantizeLinear"(%3950, %3951, %3952) : (!torch.vtensor<[3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],si8> 
    %3954 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2483> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3955 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2484> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3956 = torch.operator "onnx.DequantizeLinear"(%3953, %3954, %3955) : (!torch.vtensor<[3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],f32> 
    %3957 = torch.operator "onnx.Conv"(%3942, %3949, %3956) {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<[2,256,?,?],f32>, !torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[3],f32>) -> !torch.vtensor<[2,3,?,?],f32> 
    %3958 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2485> : tensor<12x256x1x1xf32>} : () -> !torch.vtensor<[12,256,1,1],f32> 
    %3959 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2486> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3960 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2487> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3961 = torch.operator "onnx.QuantizeLinear"(%3958, %3959, %3960) : (!torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],si8> 
    %3962 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2488> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3963 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2489> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3964 = torch.operator "onnx.DequantizeLinear"(%3961, %3962, %3963) : (!torch.vtensor<[12,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],f32> 
    %3965 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2490> : tensor<12xf32>} : () -> !torch.vtensor<[12],f32> 
    %3966 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2491> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3967 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2492> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3968 = torch.operator "onnx.QuantizeLinear"(%3965, %3966, %3967) : (!torch.vtensor<[12],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],si8> 
    %3969 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2493> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3970 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2494> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3971 = torch.operator "onnx.DequantizeLinear"(%3968, %3969, %3970) : (!torch.vtensor<[12],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],f32> 
    %3972 = torch.operator "onnx.Conv"(%3942, %3964, %3971) {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<[2,256,?,?],f32>, !torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[12],f32>) -> !torch.vtensor<[2,12,?,?],f32> 
    %3973 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2495> : tensor<256x256x3x3xf32>} : () -> !torch.vtensor<[256,256,3,3],f32> 
    %3974 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2496> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3975 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2497> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3976 = torch.operator "onnx.QuantizeLinear"(%3973, %3974, %3975) : (!torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],si8> 
    %3977 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2498> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3978 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2499> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3979 = torch.operator "onnx.DequantizeLinear"(%3976, %3977, %3978) : (!torch.vtensor<[256,256,3,3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256,256,3,3],f32> 
    %3980 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2500> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3981 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2501> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3982 = torch.operator "onnx.QuantizeLinear"(%9, %3980, %3981) : (!torch.vtensor<[256],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],si8> 
    %3983 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2502> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3984 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2503> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3985 = torch.operator "onnx.DequantizeLinear"(%3982, %3983, %3984) : (!torch.vtensor<[256],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[256],f32> 
    %3986 = torch.operator "onnx.Conv"(%3809, %3979, %3985) {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<[2,256,?,?],f32>, !torch.vtensor<[256,256,3,3],f32>, !torch.vtensor<[256],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3987 = torch.operator "onnx.Relu"(%3986) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3988 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2504> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3989 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2505> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3990 = torch.operator "onnx.QuantizeLinear"(%3987, %3988, %3989) : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],si8> 
    %3991 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2506> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3992 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2507> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3993 = torch.operator "onnx.DequantizeLinear"(%3990, %3991, %3992) : (!torch.vtensor<[2,256,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,256,?,?],f32> 
    %3994 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2508> : tensor<3x256x1x1xf32>} : () -> !torch.vtensor<[3,256,1,1],f32> 
    %3995 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2509> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3996 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2510> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %3997 = torch.operator "onnx.QuantizeLinear"(%3994, %3995, %3996) : (!torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],si8> 
    %3998 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2511> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %3999 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2512> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4000 = torch.operator "onnx.DequantizeLinear"(%3997, %3998, %3999) : (!torch.vtensor<[3,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3,256,1,1],f32> 
    %4001 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2513> : tensor<3xf32>} : () -> !torch.vtensor<[3],f32> 
    %4002 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2514> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4003 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2515> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4004 = torch.operator "onnx.QuantizeLinear"(%4001, %4002, %4003) : (!torch.vtensor<[3],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],si8> 
    %4005 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2516> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4006 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2517> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4007 = torch.operator "onnx.DequantizeLinear"(%4004, %4005, %4006) : (!torch.vtensor<[3],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[3],f32> 
    %4008 = torch.operator "onnx.Conv"(%3993, %4000, %4007) {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<[2,256,?,?],f32>, !torch.vtensor<[3,256,1,1],f32>, !torch.vtensor<[3],f32>) -> !torch.vtensor<[2,3,?,?],f32> 
    %4009 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2518> : tensor<12x256x1x1xf32>} : () -> !torch.vtensor<[12,256,1,1],f32> 
    %4010 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2519> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4011 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2520> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4012 = torch.operator "onnx.QuantizeLinear"(%4009, %4010, %4011) : (!torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],si8> 
    %4013 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2521> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4014 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2522> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4015 = torch.operator "onnx.DequantizeLinear"(%4012, %4013, %4014) : (!torch.vtensor<[12,256,1,1],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12,256,1,1],f32> 
    %4016 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2523> : tensor<12xf32>} : () -> !torch.vtensor<[12],f32> 
    %4017 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2524> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4018 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2525> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4019 = torch.operator "onnx.QuantizeLinear"(%4016, %4017, %4018) : (!torch.vtensor<[12],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],si8> 
    %4020 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2526> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4021 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2527> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4022 = torch.operator "onnx.DequantizeLinear"(%4019, %4020, %4021) : (!torch.vtensor<[12],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[12],f32> 
    %4023 = torch.operator "onnx.Conv"(%3993, %4015, %4022) {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<[2,256,?,?],f32>, !torch.vtensor<[12,256,1,1],f32>, !torch.vtensor<[12],f32>) -> !torch.vtensor<[2,12,?,?],f32> 
    %4024 = torch.operator "onnx.Shape"(%3802) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4025 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2528> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4026 = torch.operator "onnx.Gather"(%4024, %4025) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4027 = torch.operator "onnx.Shape"(%3802) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4028 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2529> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4029 = torch.operator "onnx.Gather"(%4027, %4028) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4030 = torch.operator "onnx.Shape"(%3756) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4031 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2530> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4032 = torch.operator "onnx.Gather"(%4030, %4031) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4033 = torch.operator "onnx.Shape"(%3756) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4034 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2531> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4035 = torch.operator "onnx.Gather"(%4033, %4034) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4036 = torch.operator "onnx.Shape"(%3710) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4037 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2532> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4038 = torch.operator "onnx.Gather"(%4036, %4037) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4039 = torch.operator "onnx.Shape"(%3710) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4040 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2533> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4041 = torch.operator "onnx.Gather"(%4039, %4040) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4042 = torch.operator "onnx.Shape"(%3664) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4043 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2534> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4044 = torch.operator "onnx.Gather"(%4042, %4043) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4045 = torch.operator "onnx.Shape"(%3664) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4046 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2535> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4047 = torch.operator "onnx.Gather"(%4045, %4046) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4048 = torch.operator "onnx.Shape"(%3809) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4049 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2536> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4050 = torch.operator "onnx.Gather"(%4048, %4049) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4051 = torch.operator "onnx.Shape"(%3809) : (!torch.vtensor<[2,256,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4052 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2537> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4053 = torch.operator "onnx.Gather"(%4051, %4052) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4054 = torch.operator "onnx.Shape"(%1415) : (!torch.vtensor<[2,?,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4055 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2538> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4056 = torch.operator "onnx.Gather"(%4054, %4055) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4057 = torch.operator "onnx.Shape"(%1415) : (!torch.vtensor<[2,?,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4058 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2539> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4059 = torch.operator "onnx.Gather"(%4057, %4058) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4060 = torch.operator "onnx.Div"(%4056, %4026) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4061 = torch.operator "onnx.Cast"(%4060) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4062 = torch.operator "onnx.Cast"(%4061) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4063 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2540> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4064 = torch.operator "onnx.ConstantOfShape"(%4063) {torch.onnx.value = dense_resource<__2541> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4065 = torch.operator "onnx.Shape"(%4064) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4066 = torch.operator "onnx.ConstantOfShape"(%4065) {torch.onnx.value = dense_resource<__2542> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4067 = torch.operator "onnx.Cast"(%4062) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4068 = torch.operator "onnx.Add"(%4066, %4067) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4069 = torch.operator "onnx.Div"(%4059, %4029) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4070 = torch.operator "onnx.Cast"(%4069) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4071 = torch.operator "onnx.Cast"(%4070) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4072 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2543> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4073 = torch.operator "onnx.ConstantOfShape"(%4072) {torch.onnx.value = dense_resource<__2544> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4074 = torch.operator "onnx.Shape"(%4073) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4075 = torch.operator "onnx.ConstantOfShape"(%4074) {torch.onnx.value = dense_resource<__2545> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4076 = torch.operator "onnx.Cast"(%4071) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4077 = torch.operator "onnx.Add"(%4075, %4076) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4078 = torch.operator "onnx.Div"(%4056, %4032) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4079 = torch.operator "onnx.Cast"(%4078) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4080 = torch.operator "onnx.Cast"(%4079) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4081 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2546> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4082 = torch.operator "onnx.ConstantOfShape"(%4081) {torch.onnx.value = dense_resource<__2547> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4083 = torch.operator "onnx.Shape"(%4082) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4084 = torch.operator "onnx.ConstantOfShape"(%4083) {torch.onnx.value = dense_resource<__2548> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4085 = torch.operator "onnx.Cast"(%4080) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4086 = torch.operator "onnx.Add"(%4084, %4085) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4087 = torch.operator "onnx.Div"(%4059, %4035) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4088 = torch.operator "onnx.Cast"(%4087) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4089 = torch.operator "onnx.Cast"(%4088) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4090 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2549> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4091 = torch.operator "onnx.ConstantOfShape"(%4090) {torch.onnx.value = dense_resource<__2550> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4092 = torch.operator "onnx.Shape"(%4091) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4093 = torch.operator "onnx.ConstantOfShape"(%4092) {torch.onnx.value = dense_resource<__2551> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4094 = torch.operator "onnx.Cast"(%4089) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4095 = torch.operator "onnx.Add"(%4093, %4094) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4096 = torch.operator "onnx.Div"(%4056, %4038) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4097 = torch.operator "onnx.Cast"(%4096) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4098 = torch.operator "onnx.Cast"(%4097) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4099 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2552> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4100 = torch.operator "onnx.ConstantOfShape"(%4099) {torch.onnx.value = dense_resource<__2553> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4101 = torch.operator "onnx.Shape"(%4100) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4102 = torch.operator "onnx.ConstantOfShape"(%4101) {torch.onnx.value = dense_resource<__2554> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4103 = torch.operator "onnx.Cast"(%4098) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4104 = torch.operator "onnx.Add"(%4102, %4103) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4105 = torch.operator "onnx.Div"(%4059, %4041) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4106 = torch.operator "onnx.Cast"(%4105) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4107 = torch.operator "onnx.Cast"(%4106) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4108 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2555> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4109 = torch.operator "onnx.ConstantOfShape"(%4108) {torch.onnx.value = dense_resource<__2556> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4110 = torch.operator "onnx.Shape"(%4109) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4111 = torch.operator "onnx.ConstantOfShape"(%4110) {torch.onnx.value = dense_resource<__2557> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4112 = torch.operator "onnx.Cast"(%4107) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4113 = torch.operator "onnx.Add"(%4111, %4112) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4114 = torch.operator "onnx.Div"(%4056, %4044) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4115 = torch.operator "onnx.Cast"(%4114) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4116 = torch.operator "onnx.Cast"(%4115) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4117 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2558> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4118 = torch.operator "onnx.ConstantOfShape"(%4117) {torch.onnx.value = dense_resource<__2559> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4119 = torch.operator "onnx.Shape"(%4118) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4120 = torch.operator "onnx.ConstantOfShape"(%4119) {torch.onnx.value = dense_resource<__2560> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4121 = torch.operator "onnx.Cast"(%4116) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4122 = torch.operator "onnx.Add"(%4120, %4121) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4123 = torch.operator "onnx.Div"(%4059, %4047) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4124 = torch.operator "onnx.Cast"(%4123) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4125 = torch.operator "onnx.Cast"(%4124) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4126 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2561> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4127 = torch.operator "onnx.ConstantOfShape"(%4126) {torch.onnx.value = dense_resource<__2562> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4128 = torch.operator "onnx.Shape"(%4127) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4129 = torch.operator "onnx.ConstantOfShape"(%4128) {torch.onnx.value = dense_resource<__2563> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4130 = torch.operator "onnx.Cast"(%4125) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4131 = torch.operator "onnx.Add"(%4129, %4130) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4132 = torch.operator "onnx.Div"(%4056, %4050) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4133 = torch.operator "onnx.Cast"(%4132) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4134 = torch.operator "onnx.Cast"(%4133) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4135 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2564> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4136 = torch.operator "onnx.ConstantOfShape"(%4135) {torch.onnx.value = dense_resource<__2565> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4137 = torch.operator "onnx.Shape"(%4136) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4138 = torch.operator "onnx.ConstantOfShape"(%4137) {torch.onnx.value = dense_resource<__2566> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4139 = torch.operator "onnx.Cast"(%4134) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4140 = torch.operator "onnx.Add"(%4138, %4139) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4141 = torch.operator "onnx.Div"(%4059, %4053) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4142 = torch.operator "onnx.Cast"(%4141) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4143 = torch.operator "onnx.Cast"(%4142) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4144 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2567> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
    %4145 = torch.operator "onnx.ConstantOfShape"(%4144) {torch.onnx.value = dense_resource<__2568> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4146 = torch.operator "onnx.Shape"(%4145) : (!torch.vtensor<[],si64>) -> !torch.vtensor<[0],si64> 
    %4147 = torch.operator "onnx.ConstantOfShape"(%4146) {torch.onnx.value = dense_resource<__2569> : tensor<1xsi64>} : (!torch.vtensor<[0],si64>) -> !torch.vtensor<[],si64> 
    %4148 = torch.operator "onnx.Cast"(%4143) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4149 = torch.operator "onnx.Add"(%4147, %4148) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4150 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2570> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4151 = torch.operator "onnx.Cast"(%4029) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4152 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2571> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4153 = torch.operator "onnx.Range"(%4150, %4151, %4152) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4154 = torch.operator "onnx.Cast"(%4077) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4155 = torch.operator "onnx.Mul"(%4153, %4154) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4156 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2572> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4157 = torch.operator "onnx.Cast"(%4026) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4158 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2573> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4159 = torch.operator "onnx.Range"(%4156, %4157, %4158) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4160 = torch.operator "onnx.Cast"(%4068) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4161 = torch.operator "onnx.Mul"(%4159, %4160) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4162 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2574> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4163 = torch.operator "onnx.Reshape"(%4161, %4162) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4164 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2575> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4165 = torch.operator "onnx.Reshape"(%4155, %4164) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4166 = torch.operator "onnx.Shape"(%4163) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4167 = torch.operator "onnx.Shape"(%4165) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4168 = torch.operator "onnx.Concat"(%4166, %4167) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4169 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2576> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4170 = torch.operator "onnx.Concat"(%4166, %4169) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4171 = torch.operator "onnx.Reshape"(%4163, %4170) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,1],si32> 
    %4172 = torch.operator "onnx.Expand"(%4171, %4168) : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4173 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2577> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4174 = torch.operator "onnx.Concat"(%4173, %4167) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4175 = torch.operator "onnx.Reshape"(%4165, %4174) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[1,?],si32> 
    %4176 = torch.operator "onnx.Expand"(%4175, %4168) : (!torch.vtensor<[1,?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4177 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2578> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4178 = torch.operator "onnx.Reshape"(%4176, %4177) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4179 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2579> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4180 = torch.operator "onnx.Reshape"(%4172, %4179) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4181 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2580> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4182 = torch.operator "onnx.Unsqueeze"(%4178, %4181) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4183 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2581> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4184 = torch.operator "onnx.Unsqueeze"(%4180, %4183) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4185 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2582> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4186 = torch.operator "onnx.Unsqueeze"(%4178, %4185) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4187 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2583> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4188 = torch.operator "onnx.Unsqueeze"(%4180, %4187) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4189 = torch.operator "onnx.Concat"(%4182, %4184, %4186, %4188) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>) -> !torch.vtensor<[?,4],si32> 
    %4190 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2584> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64> 
    %4191 = torch.operator "onnx.Reshape"(%4189, %4190) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,4],si32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,1,4],si32> 
    %4192 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2585> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4193 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2586> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4194 = torch.operator "onnx.QuantizeLinear"(%8, %4192, %4193) : (!torch.vtensor<[1,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],si8> 
    %4195 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2587> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4196 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2588> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4197 = torch.operator "onnx.DequantizeLinear"(%4194, %4195, %4196) : (!torch.vtensor<[1,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],f32> 
    %4198 = torch.operator "onnx.Cast"(%4191) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,1,4],si32>) -> !torch.vtensor<[?,1,4],f32> 
    %4199 = torch.operator "onnx.Add"(%4198, %4197) : (!torch.vtensor<[?,1,4],f32>, !torch.vtensor<[1,3,4],f32>) -> !torch.vtensor<[?,3,4],f32> 
    %4200 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2589> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4201 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2590> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4202 = torch.operator "onnx.QuantizeLinear"(%4199, %4200, %4201) : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],si8> 
    %4203 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2591> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4204 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2592> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4205 = torch.operator "onnx.DequantizeLinear"(%4202, %4203, %4204) : (!torch.vtensor<[?,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],f32> 
    %4206 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2593> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %4207 = torch.operator "onnx.Reshape"(%4205, %4206) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %4208 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2594> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4209 = torch.operator "onnx.Cast"(%4035) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4210 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2595> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4211 = torch.operator "onnx.Range"(%4208, %4209, %4210) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4212 = torch.operator "onnx.Cast"(%4095) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4213 = torch.operator "onnx.Mul"(%4211, %4212) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4214 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2596> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4215 = torch.operator "onnx.Cast"(%4032) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4216 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2597> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4217 = torch.operator "onnx.Range"(%4214, %4215, %4216) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4218 = torch.operator "onnx.Cast"(%4086) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4219 = torch.operator "onnx.Mul"(%4217, %4218) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4220 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2598> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4221 = torch.operator "onnx.Reshape"(%4219, %4220) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4222 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2599> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4223 = torch.operator "onnx.Reshape"(%4213, %4222) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4224 = torch.operator "onnx.Shape"(%4221) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4225 = torch.operator "onnx.Shape"(%4223) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4226 = torch.operator "onnx.Concat"(%4224, %4225) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4227 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2600> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4228 = torch.operator "onnx.Concat"(%4224, %4227) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4229 = torch.operator "onnx.Reshape"(%4221, %4228) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,1],si32> 
    %4230 = torch.operator "onnx.Expand"(%4229, %4226) : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4231 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2601> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4232 = torch.operator "onnx.Concat"(%4231, %4225) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4233 = torch.operator "onnx.Reshape"(%4223, %4232) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[1,?],si32> 
    %4234 = torch.operator "onnx.Expand"(%4233, %4226) : (!torch.vtensor<[1,?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4235 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2602> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4236 = torch.operator "onnx.Reshape"(%4234, %4235) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4237 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2603> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4238 = torch.operator "onnx.Reshape"(%4230, %4237) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4239 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2604> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4240 = torch.operator "onnx.Unsqueeze"(%4236, %4239) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4241 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2605> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4242 = torch.operator "onnx.Unsqueeze"(%4238, %4241) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4243 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2606> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4244 = torch.operator "onnx.Unsqueeze"(%4236, %4243) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4245 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2607> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4246 = torch.operator "onnx.Unsqueeze"(%4238, %4245) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4247 = torch.operator "onnx.Concat"(%4240, %4242, %4244, %4246) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>) -> !torch.vtensor<[?,4],si32> 
    %4248 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2608> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64> 
    %4249 = torch.operator "onnx.Reshape"(%4247, %4248) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,4],si32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,1,4],si32> 
    %4250 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2609> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4251 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2610> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4252 = torch.operator "onnx.QuantizeLinear"(%7, %4250, %4251) : (!torch.vtensor<[1,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],si8> 
    %4253 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2611> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4254 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2612> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4255 = torch.operator "onnx.DequantizeLinear"(%4252, %4253, %4254) : (!torch.vtensor<[1,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],f32> 
    %4256 = torch.operator "onnx.Cast"(%4249) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,1,4],si32>) -> !torch.vtensor<[?,1,4],f32> 
    %4257 = torch.operator "onnx.Add"(%4256, %4255) : (!torch.vtensor<[?,1,4],f32>, !torch.vtensor<[1,3,4],f32>) -> !torch.vtensor<[?,3,4],f32> 
    %4258 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2613> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4259 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2614> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4260 = torch.operator "onnx.QuantizeLinear"(%4257, %4258, %4259) : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],si8> 
    %4261 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2615> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4262 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2616> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4263 = torch.operator "onnx.DequantizeLinear"(%4260, %4261, %4262) : (!torch.vtensor<[?,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],f32> 
    %4264 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2617> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %4265 = torch.operator "onnx.Reshape"(%4263, %4264) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %4266 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2618> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4267 = torch.operator "onnx.Cast"(%4041) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4268 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2619> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4269 = torch.operator "onnx.Range"(%4266, %4267, %4268) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4270 = torch.operator "onnx.Cast"(%4113) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4271 = torch.operator "onnx.Mul"(%4269, %4270) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4272 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2620> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4273 = torch.operator "onnx.Cast"(%4038) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4274 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2621> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4275 = torch.operator "onnx.Range"(%4272, %4273, %4274) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4276 = torch.operator "onnx.Cast"(%4104) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4277 = torch.operator "onnx.Mul"(%4275, %4276) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4278 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2622> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4279 = torch.operator "onnx.Reshape"(%4277, %4278) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4280 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2623> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4281 = torch.operator "onnx.Reshape"(%4271, %4280) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4282 = torch.operator "onnx.Shape"(%4279) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4283 = torch.operator "onnx.Shape"(%4281) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4284 = torch.operator "onnx.Concat"(%4282, %4283) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4285 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2624> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4286 = torch.operator "onnx.Concat"(%4282, %4285) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4287 = torch.operator "onnx.Reshape"(%4279, %4286) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,1],si32> 
    %4288 = torch.operator "onnx.Expand"(%4287, %4284) : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4289 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2625> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4290 = torch.operator "onnx.Concat"(%4289, %4283) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4291 = torch.operator "onnx.Reshape"(%4281, %4290) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[1,?],si32> 
    %4292 = torch.operator "onnx.Expand"(%4291, %4284) : (!torch.vtensor<[1,?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4293 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2626> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4294 = torch.operator "onnx.Reshape"(%4292, %4293) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4295 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2627> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4296 = torch.operator "onnx.Reshape"(%4288, %4295) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4297 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2628> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4298 = torch.operator "onnx.Unsqueeze"(%4294, %4297) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4299 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2629> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4300 = torch.operator "onnx.Unsqueeze"(%4296, %4299) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4301 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2630> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4302 = torch.operator "onnx.Unsqueeze"(%4294, %4301) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4303 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2631> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4304 = torch.operator "onnx.Unsqueeze"(%4296, %4303) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4305 = torch.operator "onnx.Concat"(%4298, %4300, %4302, %4304) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>) -> !torch.vtensor<[?,4],si32> 
    %4306 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2632> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64> 
    %4307 = torch.operator "onnx.Reshape"(%4305, %4306) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,4],si32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,1,4],si32> 
    %4308 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2633> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4309 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2634> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4310 = torch.operator "onnx.QuantizeLinear"(%6, %4308, %4309) : (!torch.vtensor<[1,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],si8> 
    %4311 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2635> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4312 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2636> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4313 = torch.operator "onnx.DequantizeLinear"(%4310, %4311, %4312) : (!torch.vtensor<[1,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],f32> 
    %4314 = torch.operator "onnx.Cast"(%4307) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,1,4],si32>) -> !torch.vtensor<[?,1,4],f32> 
    %4315 = torch.operator "onnx.Add"(%4314, %4313) : (!torch.vtensor<[?,1,4],f32>, !torch.vtensor<[1,3,4],f32>) -> !torch.vtensor<[?,3,4],f32> 
    %4316 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2637> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4317 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2638> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4318 = torch.operator "onnx.QuantizeLinear"(%4315, %4316, %4317) : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],si8> 
    %4319 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2639> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4320 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2640> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4321 = torch.operator "onnx.DequantizeLinear"(%4318, %4319, %4320) : (!torch.vtensor<[?,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],f32> 
    %4322 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2641> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %4323 = torch.operator "onnx.Reshape"(%4321, %4322) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %4324 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2642> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4325 = torch.operator "onnx.Cast"(%4047) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4326 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2643> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4327 = torch.operator "onnx.Range"(%4324, %4325, %4326) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4328 = torch.operator "onnx.Cast"(%4131) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4329 = torch.operator "onnx.Mul"(%4327, %4328) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4330 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2644> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4331 = torch.operator "onnx.Cast"(%4044) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4332 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2645> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4333 = torch.operator "onnx.Range"(%4330, %4331, %4332) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4334 = torch.operator "onnx.Cast"(%4122) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4335 = torch.operator "onnx.Mul"(%4333, %4334) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4336 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2646> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4337 = torch.operator "onnx.Reshape"(%4335, %4336) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4338 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2647> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4339 = torch.operator "onnx.Reshape"(%4329, %4338) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4340 = torch.operator "onnx.Shape"(%4337) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4341 = torch.operator "onnx.Shape"(%4339) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4342 = torch.operator "onnx.Concat"(%4340, %4341) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4343 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2648> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4344 = torch.operator "onnx.Concat"(%4340, %4343) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4345 = torch.operator "onnx.Reshape"(%4337, %4344) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,1],si32> 
    %4346 = torch.operator "onnx.Expand"(%4345, %4342) : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4347 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2649> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4348 = torch.operator "onnx.Concat"(%4347, %4341) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4349 = torch.operator "onnx.Reshape"(%4339, %4348) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[1,?],si32> 
    %4350 = torch.operator "onnx.Expand"(%4349, %4342) : (!torch.vtensor<[1,?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4351 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2650> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4352 = torch.operator "onnx.Reshape"(%4350, %4351) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4353 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2651> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4354 = torch.operator "onnx.Reshape"(%4346, %4353) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4355 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2652> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4356 = torch.operator "onnx.Unsqueeze"(%4352, %4355) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4357 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2653> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4358 = torch.operator "onnx.Unsqueeze"(%4354, %4357) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4359 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2654> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4360 = torch.operator "onnx.Unsqueeze"(%4352, %4359) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4361 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2655> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4362 = torch.operator "onnx.Unsqueeze"(%4354, %4361) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4363 = torch.operator "onnx.Concat"(%4356, %4358, %4360, %4362) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>) -> !torch.vtensor<[?,4],si32> 
    %4364 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2656> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64> 
    %4365 = torch.operator "onnx.Reshape"(%4363, %4364) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,4],si32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,1,4],si32> 
    %4366 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2657> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4367 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2658> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4368 = torch.operator "onnx.QuantizeLinear"(%5, %4366, %4367) : (!torch.vtensor<[1,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],si8> 
    %4369 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2659> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4370 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2660> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4371 = torch.operator "onnx.DequantizeLinear"(%4368, %4369, %4370) : (!torch.vtensor<[1,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],f32> 
    %4372 = torch.operator "onnx.Cast"(%4365) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,1,4],si32>) -> !torch.vtensor<[?,1,4],f32> 
    %4373 = torch.operator "onnx.Add"(%4372, %4371) : (!torch.vtensor<[?,1,4],f32>, !torch.vtensor<[1,3,4],f32>) -> !torch.vtensor<[?,3,4],f32> 
    %4374 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2661> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4375 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2662> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4376 = torch.operator "onnx.QuantizeLinear"(%4373, %4374, %4375) : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],si8> 
    %4377 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2663> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4378 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2664> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4379 = torch.operator "onnx.DequantizeLinear"(%4376, %4377, %4378) : (!torch.vtensor<[?,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],f32> 
    %4380 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2665> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %4381 = torch.operator "onnx.Reshape"(%4379, %4380) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %4382 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2666> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4383 = torch.operator "onnx.Cast"(%4053) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4384 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2667> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4385 = torch.operator "onnx.Range"(%4382, %4383, %4384) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4386 = torch.operator "onnx.Cast"(%4149) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4387 = torch.operator "onnx.Mul"(%4385, %4386) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4388 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2668> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4389 = torch.operator "onnx.Cast"(%4050) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4390 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2669> : tensor<si32>} : () -> !torch.vtensor<[],si32> 
    %4391 = torch.operator "onnx.Range"(%4388, %4389, %4390) : (!torch.vtensor<[],si32>, !torch.vtensor<[],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4392 = torch.operator "onnx.Cast"(%4140) {torch.onnx.to = 6 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si32> 
    %4393 = torch.operator "onnx.Mul"(%4391, %4392) : (!torch.vtensor<[?],si32>, !torch.vtensor<[],si32>) -> !torch.vtensor<[?],si32> 
    %4394 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2670> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4395 = torch.operator "onnx.Reshape"(%4393, %4394) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4396 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2671> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4397 = torch.operator "onnx.Reshape"(%4387, %4396) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4398 = torch.operator "onnx.Shape"(%4395) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4399 = torch.operator "onnx.Shape"(%4397) : (!torch.vtensor<[?],si32>) -> !torch.vtensor<[1],si64> 
    %4400 = torch.operator "onnx.Concat"(%4398, %4399) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4401 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2672> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4402 = torch.operator "onnx.Concat"(%4398, %4401) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4403 = torch.operator "onnx.Reshape"(%4395, %4402) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,1],si32> 
    %4404 = torch.operator "onnx.Expand"(%4403, %4400) : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4405 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2673> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4406 = torch.operator "onnx.Concat"(%4405, %4399) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4407 = torch.operator "onnx.Reshape"(%4397, %4406) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[1,?],si32> 
    %4408 = torch.operator "onnx.Expand"(%4407, %4400) : (!torch.vtensor<[1,?],si32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si32> 
    %4409 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2674> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4410 = torch.operator "onnx.Reshape"(%4408, %4409) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4411 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2675> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4412 = torch.operator "onnx.Reshape"(%4404, %4411) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si32> 
    %4413 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2676> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4414 = torch.operator "onnx.Unsqueeze"(%4410, %4413) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4415 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2677> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4416 = torch.operator "onnx.Unsqueeze"(%4412, %4415) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4417 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2678> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4418 = torch.operator "onnx.Unsqueeze"(%4410, %4417) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4419 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2679> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4420 = torch.operator "onnx.Unsqueeze"(%4412, %4419) : (!torch.vtensor<[?],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si32> 
    %4421 = torch.operator "onnx.Concat"(%4414, %4416, %4418, %4420) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>, !torch.vtensor<[?,1],si32>) -> !torch.vtensor<[?,4],si32> 
    %4422 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2680> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64> 
    %4423 = torch.operator "onnx.Reshape"(%4421, %4422) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,4],si32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,1,4],si32> 
    %4424 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2681> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4425 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2682> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4426 = torch.operator "onnx.QuantizeLinear"(%4, %4424, %4425) : (!torch.vtensor<[1,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],si8> 
    %4427 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2683> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4428 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2684> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4429 = torch.operator "onnx.DequantizeLinear"(%4426, %4427, %4428) : (!torch.vtensor<[1,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[1,3,4],f32> 
    %4430 = torch.operator "onnx.Cast"(%4423) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,1,4],si32>) -> !torch.vtensor<[?,1,4],f32> 
    %4431 = torch.operator "onnx.Add"(%4430, %4429) : (!torch.vtensor<[?,1,4],f32>, !torch.vtensor<[1,3,4],f32>) -> !torch.vtensor<[?,3,4],f32> 
    %4432 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2685> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4433 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2686> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4434 = torch.operator "onnx.QuantizeLinear"(%4431, %4432, %4433) : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],si8> 
    %4435 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2687> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4436 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2688> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4437 = torch.operator "onnx.DequantizeLinear"(%4434, %4435, %4436) : (!torch.vtensor<[?,3,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,3,4],f32> 
    %4438 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2689> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %4439 = torch.operator "onnx.Reshape"(%4437, %4438) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,3,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %4440 = torch.operator "onnx.Concat"(%4207, %4265, %4323, %4381, %4439) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>) -> !torch.vtensor<[?,4],f32> 
    %4441 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2690> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4442 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2691> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4443 = torch.operator "onnx.QuantizeLinear"(%4440, %4441, %4442) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],si8> 
    %4444 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2692> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4445 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2693> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4446 = torch.operator "onnx.DequantizeLinear"(%4443, %4444, %4445) : (!torch.vtensor<[?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],f32> 
    %4447 = torch.operator "onnx.Concat"(%4207, %4265, %4323, %4381, %4439) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>) -> !torch.vtensor<[?,4],f32> 
    %4448 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2694> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4449 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2695> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4450 = torch.operator "onnx.QuantizeLinear"(%4447, %4448, %4449) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],si8> 
    %4451 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2696> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4452 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2697> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4453 = torch.operator "onnx.DequantizeLinear"(%4450, %4451, %4452) : (!torch.vtensor<[?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],f32> 
    %4454 = torch.operator "onnx.Gather"(%3818, %284) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[3,?,?],f32> 
    %4455 = torch.operator "onnx.Shape"(%4454) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4456 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2698> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4457 = torch.operator "onnx.Gather"(%4455, %4456) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4458 = torch.operator "onnx.Shape"(%4454) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4459 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2699> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4460 = torch.operator "onnx.Gather"(%4458, %4459) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4461 = torch.operator "onnx.Shape"(%4454) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4462 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2700> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4463 = torch.operator "onnx.Gather"(%4461, %4462) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4464 = torch.operator "onnx.Gather"(%3855, %284) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[3,?,?],f32> 
    %4465 = torch.operator "onnx.Shape"(%4464) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4466 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2701> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4467 = torch.operator "onnx.Gather"(%4465, %4466) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4468 = torch.operator "onnx.Shape"(%4464) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4469 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2702> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4470 = torch.operator "onnx.Gather"(%4468, %4469) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4471 = torch.operator "onnx.Shape"(%4464) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4472 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2703> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4473 = torch.operator "onnx.Gather"(%4471, %4472) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4474 = torch.operator "onnx.Gather"(%3906, %284) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[3,?,?],f32> 
    %4475 = torch.operator "onnx.Shape"(%4474) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4476 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2704> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4477 = torch.operator "onnx.Gather"(%4475, %4476) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4478 = torch.operator "onnx.Shape"(%4474) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4479 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2705> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4480 = torch.operator "onnx.Gather"(%4478, %4479) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4481 = torch.operator "onnx.Shape"(%4474) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4482 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2706> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4483 = torch.operator "onnx.Gather"(%4481, %4482) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4484 = torch.operator "onnx.Gather"(%3957, %284) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[3,?,?],f32> 
    %4485 = torch.operator "onnx.Shape"(%4484) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4486 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2707> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4487 = torch.operator "onnx.Gather"(%4485, %4486) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4488 = torch.operator "onnx.Shape"(%4484) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4489 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2708> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4490 = torch.operator "onnx.Gather"(%4488, %4489) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4491 = torch.operator "onnx.Shape"(%4484) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4492 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2709> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4493 = torch.operator "onnx.Gather"(%4491, %4492) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4494 = torch.operator "onnx.Gather"(%4008, %284) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[3,?,?],f32> 
    %4495 = torch.operator "onnx.Shape"(%4494) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4496 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2710> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4497 = torch.operator "onnx.Gather"(%4495, %4496) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4498 = torch.operator "onnx.Shape"(%4494) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4499 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2711> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4500 = torch.operator "onnx.Gather"(%4498, %4499) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4501 = torch.operator "onnx.Shape"(%4494) : (!torch.vtensor<[3,?,?],f32>) -> !torch.vtensor<[3],si64> 
    %4502 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2712> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4503 = torch.operator "onnx.Gather"(%4501, %4502) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4504 = torch.operator "onnx.Mul"(%4457, %4460) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4505 = torch.operator "onnx.Mul"(%4504, %4463) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4506 = torch.operator "onnx.Mul"(%4467, %4470) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4507 = torch.operator "onnx.Mul"(%4506, %4473) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4508 = torch.operator "onnx.Mul"(%4477, %4480) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4509 = torch.operator "onnx.Mul"(%4508, %4483) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4510 = torch.operator "onnx.Mul"(%4487, %4490) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4511 = torch.operator "onnx.Mul"(%4510, %4493) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4512 = torch.operator "onnx.Mul"(%4497, %4500) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4513 = torch.operator "onnx.Mul"(%4512, %4503) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4514 = torch.operator "onnx.Shape"(%3818) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4515 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2713> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4516 = torch.operator "onnx.Gather"(%4514, %4515) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4517 = torch.operator "onnx.Shape"(%3818) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4518 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2714> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4519 = torch.operator "onnx.Gather"(%4517, %4518) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4520 = torch.operator "onnx.Shape"(%3818) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4521 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2715> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4522 = torch.operator "onnx.Gather"(%4520, %4521) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4523 = torch.operator "onnx.Shape"(%3818) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4524 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2716> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4525 = torch.operator "onnx.Gather"(%4523, %4524) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4526 = torch.operator "onnx.Shape"(%3819) : (!torch.vtensor<[2,12,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4527 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2717> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4528 = torch.operator "onnx.Gather"(%4526, %4527) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4529 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2718> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4530 = torch.operator "onnx.Div"(%4528, %4529) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4531 = torch.operator "onnx.Cast"(%4530) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4532 = torch.operator "onnx.Cast"(%4531) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4533 = torch.operator "onnx.Div"(%4519, %4532) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4534 = torch.operator "onnx.Cast"(%4533) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4535 = torch.operator "onnx.Cast"(%4534) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4536 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2719> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4537 = torch.operator "onnx.Unsqueeze"(%4516, %4536) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4538 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2720> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4539 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2721> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4540 = torch.operator "onnx.Unsqueeze"(%4535, %4539) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4541 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2722> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4542 = torch.operator "onnx.Unsqueeze"(%4522, %4541) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4543 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2723> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4544 = torch.operator "onnx.Unsqueeze"(%4525, %4543) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4545 = torch.operator "onnx.Concat"(%4537, %4538, %4540, %4542, %4544) {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> 
    %4546 = torch.operator "onnx.Reshape"(%3818, %4545) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4547 = torch.operator "onnx.Transpose"(%4546) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4548 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2724> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4549 = torch.operator "onnx.Unsqueeze"(%4516, %4548) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4550 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2725> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4551 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2726> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4552 = torch.operator "onnx.Unsqueeze"(%4535, %4551) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4553 = torch.operator "onnx.Concat"(%4549, %4550, %4552) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4554 = torch.operator "onnx.Reshape"(%4547, %4553) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %4555 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2727> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4556 = torch.operator "onnx.Unsqueeze"(%4516, %4555) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4557 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2728> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4558 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2729> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4559 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2730> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4560 = torch.operator "onnx.Unsqueeze"(%4522, %4559) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4561 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2731> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4562 = torch.operator "onnx.Unsqueeze"(%4525, %4561) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4563 = torch.operator "onnx.Concat"(%4556, %4557, %4558, %4560, %4562) {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> 
    %4564 = torch.operator "onnx.Reshape"(%3819, %4563) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,12,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[2,?,4,?,?],f32> 
    %4565 = torch.operator "onnx.Transpose"(%4564) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[2,?,4,?,?],f32>) -> !torch.vtensor<[2,?,?,?,4],f32> 
    %4566 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2732> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4567 = torch.operator "onnx.Unsqueeze"(%4516, %4566) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4568 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2733> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4569 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2734> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4570 = torch.operator "onnx.Concat"(%4567, %4568, %4569) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4571 = torch.operator "onnx.Reshape"(%4565, %4570) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,?,?,?,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,?,4],f32> 
    %4572 = torch.operator "onnx.Shape"(%3855) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4573 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2735> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4574 = torch.operator "onnx.Gather"(%4572, %4573) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4575 = torch.operator "onnx.Shape"(%3855) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4576 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2736> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4577 = torch.operator "onnx.Gather"(%4575, %4576) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4578 = torch.operator "onnx.Shape"(%3855) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4579 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2737> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4580 = torch.operator "onnx.Gather"(%4578, %4579) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4581 = torch.operator "onnx.Shape"(%3855) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4582 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2738> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4583 = torch.operator "onnx.Gather"(%4581, %4582) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4584 = torch.operator "onnx.Shape"(%3870) : (!torch.vtensor<[2,12,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4585 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2739> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4586 = torch.operator "onnx.Gather"(%4584, %4585) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4587 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2740> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4588 = torch.operator "onnx.Div"(%4586, %4587) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4589 = torch.operator "onnx.Cast"(%4588) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4590 = torch.operator "onnx.Cast"(%4589) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4591 = torch.operator "onnx.Div"(%4577, %4590) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4592 = torch.operator "onnx.Cast"(%4591) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4593 = torch.operator "onnx.Cast"(%4592) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4594 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2741> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4595 = torch.operator "onnx.Unsqueeze"(%4574, %4594) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4596 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2742> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4597 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2743> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4598 = torch.operator "onnx.Unsqueeze"(%4593, %4597) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4599 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2744> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4600 = torch.operator "onnx.Unsqueeze"(%4580, %4599) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4601 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2745> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4602 = torch.operator "onnx.Unsqueeze"(%4583, %4601) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4603 = torch.operator "onnx.Concat"(%4595, %4596, %4598, %4600, %4602) {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> 
    %4604 = torch.operator "onnx.Reshape"(%3855, %4603) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4605 = torch.operator "onnx.Transpose"(%4604) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4606 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2746> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4607 = torch.operator "onnx.Unsqueeze"(%4574, %4606) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4608 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2747> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4609 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2748> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4610 = torch.operator "onnx.Unsqueeze"(%4593, %4609) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4611 = torch.operator "onnx.Concat"(%4607, %4608, %4610) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4612 = torch.operator "onnx.Reshape"(%4605, %4611) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %4613 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2749> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4614 = torch.operator "onnx.Unsqueeze"(%4574, %4613) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4615 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2750> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4616 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2751> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4617 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2752> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4618 = torch.operator "onnx.Unsqueeze"(%4580, %4617) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4619 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2753> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4620 = torch.operator "onnx.Unsqueeze"(%4583, %4619) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4621 = torch.operator "onnx.Concat"(%4614, %4615, %4616, %4618, %4620) {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> 
    %4622 = torch.operator "onnx.Reshape"(%3870, %4621) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,12,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[2,?,4,?,?],f32> 
    %4623 = torch.operator "onnx.Transpose"(%4622) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[2,?,4,?,?],f32>) -> !torch.vtensor<[2,?,?,?,4],f32> 
    %4624 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2754> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4625 = torch.operator "onnx.Unsqueeze"(%4574, %4624) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4626 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2755> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2756> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4628 = torch.operator "onnx.Concat"(%4625, %4626, %4627) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4629 = torch.operator "onnx.Reshape"(%4623, %4628) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,?,?,?,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,?,4],f32> 
    %4630 = torch.operator "onnx.Shape"(%3906) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4631 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2757> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4632 = torch.operator "onnx.Gather"(%4630, %4631) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4633 = torch.operator "onnx.Shape"(%3906) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4634 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2758> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4635 = torch.operator "onnx.Gather"(%4633, %4634) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4636 = torch.operator "onnx.Shape"(%3906) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4637 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2759> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4638 = torch.operator "onnx.Gather"(%4636, %4637) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4639 = torch.operator "onnx.Shape"(%3906) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4640 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2760> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4641 = torch.operator "onnx.Gather"(%4639, %4640) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4642 = torch.operator "onnx.Shape"(%3921) : (!torch.vtensor<[2,12,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4643 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2761> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4644 = torch.operator "onnx.Gather"(%4642, %4643) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4645 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2762> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4646 = torch.operator "onnx.Div"(%4644, %4645) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4647 = torch.operator "onnx.Cast"(%4646) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4648 = torch.operator "onnx.Cast"(%4647) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4649 = torch.operator "onnx.Div"(%4635, %4648) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4650 = torch.operator "onnx.Cast"(%4649) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4651 = torch.operator "onnx.Cast"(%4650) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2763> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4653 = torch.operator "onnx.Unsqueeze"(%4632, %4652) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4654 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2764> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4655 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2765> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4656 = torch.operator "onnx.Unsqueeze"(%4651, %4655) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4657 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2766> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4658 = torch.operator "onnx.Unsqueeze"(%4638, %4657) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4659 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2767> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4660 = torch.operator "onnx.Unsqueeze"(%4641, %4659) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4661 = torch.operator "onnx.Concat"(%4653, %4654, %4656, %4658, %4660) {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> 
    %4662 = torch.operator "onnx.Reshape"(%3906, %4661) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4663 = torch.operator "onnx.Transpose"(%4662) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4664 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2768> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4665 = torch.operator "onnx.Unsqueeze"(%4632, %4664) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2769> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4667 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2770> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4668 = torch.operator "onnx.Unsqueeze"(%4651, %4667) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4669 = torch.operator "onnx.Concat"(%4665, %4666, %4668) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4670 = torch.operator "onnx.Reshape"(%4663, %4669) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %4671 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2771> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4672 = torch.operator "onnx.Unsqueeze"(%4632, %4671) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4673 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2772> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4674 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2773> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4675 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2774> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4676 = torch.operator "onnx.Unsqueeze"(%4638, %4675) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4677 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2775> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4678 = torch.operator "onnx.Unsqueeze"(%4641, %4677) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4679 = torch.operator "onnx.Concat"(%4672, %4673, %4674, %4676, %4678) {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> 
    %4680 = torch.operator "onnx.Reshape"(%3921, %4679) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,12,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[2,?,4,?,?],f32> 
    %4681 = torch.operator "onnx.Transpose"(%4680) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[2,?,4,?,?],f32>) -> !torch.vtensor<[2,?,?,?,4],f32> 
    %4682 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2776> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4683 = torch.operator "onnx.Unsqueeze"(%4632, %4682) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4684 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2777> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4685 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2778> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4686 = torch.operator "onnx.Concat"(%4683, %4684, %4685) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4687 = torch.operator "onnx.Reshape"(%4681, %4686) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,?,?,?,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,?,4],f32> 
    %4688 = torch.operator "onnx.Shape"(%3957) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4689 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2779> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4690 = torch.operator "onnx.Gather"(%4688, %4689) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4691 = torch.operator "onnx.Shape"(%3957) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4692 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2780> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4693 = torch.operator "onnx.Gather"(%4691, %4692) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4694 = torch.operator "onnx.Shape"(%3957) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4695 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2781> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4696 = torch.operator "onnx.Gather"(%4694, %4695) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4697 = torch.operator "onnx.Shape"(%3957) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4698 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2782> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4699 = torch.operator "onnx.Gather"(%4697, %4698) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4700 = torch.operator "onnx.Shape"(%3972) : (!torch.vtensor<[2,12,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4701 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2783> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4702 = torch.operator "onnx.Gather"(%4700, %4701) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4703 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2784> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4704 = torch.operator "onnx.Div"(%4702, %4703) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4705 = torch.operator "onnx.Cast"(%4704) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4706 = torch.operator "onnx.Cast"(%4705) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4707 = torch.operator "onnx.Div"(%4693, %4706) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4708 = torch.operator "onnx.Cast"(%4707) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4709 = torch.operator "onnx.Cast"(%4708) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4710 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2785> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4711 = torch.operator "onnx.Unsqueeze"(%4690, %4710) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4712 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2786> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4713 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2787> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4714 = torch.operator "onnx.Unsqueeze"(%4709, %4713) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4715 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2788> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4716 = torch.operator "onnx.Unsqueeze"(%4696, %4715) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4717 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2789> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4718 = torch.operator "onnx.Unsqueeze"(%4699, %4717) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4719 = torch.operator "onnx.Concat"(%4711, %4712, %4714, %4716, %4718) {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> 
    %4720 = torch.operator "onnx.Reshape"(%3957, %4719) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4721 = torch.operator "onnx.Transpose"(%4720) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4722 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2790> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4723 = torch.operator "onnx.Unsqueeze"(%4690, %4722) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4724 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2791> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4725 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2792> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4726 = torch.operator "onnx.Unsqueeze"(%4709, %4725) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4727 = torch.operator "onnx.Concat"(%4723, %4724, %4726) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4728 = torch.operator "onnx.Reshape"(%4721, %4727) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %4729 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2793> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4730 = torch.operator "onnx.Unsqueeze"(%4690, %4729) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4731 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2794> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4732 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2795> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4733 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2796> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4734 = torch.operator "onnx.Unsqueeze"(%4696, %4733) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4735 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2797> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4736 = torch.operator "onnx.Unsqueeze"(%4699, %4735) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4737 = torch.operator "onnx.Concat"(%4730, %4731, %4732, %4734, %4736) {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> 
    %4738 = torch.operator "onnx.Reshape"(%3972, %4737) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,12,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[2,?,4,?,?],f32> 
    %4739 = torch.operator "onnx.Transpose"(%4738) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[2,?,4,?,?],f32>) -> !torch.vtensor<[2,?,?,?,4],f32> 
    %4740 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2798> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4741 = torch.operator "onnx.Unsqueeze"(%4690, %4740) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4742 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2799> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4743 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2800> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4744 = torch.operator "onnx.Concat"(%4741, %4742, %4743) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4745 = torch.operator "onnx.Reshape"(%4739, %4744) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,?,?,?,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,?,4],f32> 
    %4746 = torch.operator "onnx.Shape"(%4008) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4747 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2801> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4748 = torch.operator "onnx.Gather"(%4746, %4747) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4749 = torch.operator "onnx.Shape"(%4008) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4750 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2802> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4751 = torch.operator "onnx.Gather"(%4749, %4750) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4752 = torch.operator "onnx.Shape"(%4008) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4753 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2803> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4754 = torch.operator "onnx.Gather"(%4752, %4753) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4755 = torch.operator "onnx.Shape"(%4008) : (!torch.vtensor<[2,3,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4756 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2804> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4757 = torch.operator "onnx.Gather"(%4755, %4756) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4758 = torch.operator "onnx.Shape"(%4023) : (!torch.vtensor<[2,12,?,?],f32>) -> !torch.vtensor<[4],si64> 
    %4759 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2805> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4760 = torch.operator "onnx.Gather"(%4758, %4759) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4761 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2806> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4762 = torch.operator "onnx.Div"(%4760, %4761) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4763 = torch.operator "onnx.Cast"(%4762) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4764 = torch.operator "onnx.Cast"(%4763) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4765 = torch.operator "onnx.Div"(%4751, %4764) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4766 = torch.operator "onnx.Cast"(%4765) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4767 = torch.operator "onnx.Cast"(%4766) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4768 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2807> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4769 = torch.operator "onnx.Unsqueeze"(%4748, %4768) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4770 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2808> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4771 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2809> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4772 = torch.operator "onnx.Unsqueeze"(%4767, %4771) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4773 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2810> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4774 = torch.operator "onnx.Unsqueeze"(%4754, %4773) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4775 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2811> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4776 = torch.operator "onnx.Unsqueeze"(%4757, %4775) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4777 = torch.operator "onnx.Concat"(%4769, %4770, %4772, %4774, %4776) {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> 
    %4778 = torch.operator "onnx.Reshape"(%4008, %4777) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,3,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4779 = torch.operator "onnx.Transpose"(%4778) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[?,?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?,?],f32> 
    %4780 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2812> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4781 = torch.operator "onnx.Unsqueeze"(%4748, %4780) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4782 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2813> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4783 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2814> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4784 = torch.operator "onnx.Unsqueeze"(%4767, %4783) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4785 = torch.operator "onnx.Concat"(%4781, %4782, %4784) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4786 = torch.operator "onnx.Reshape"(%4779, %4785) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> 
    %4787 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2815> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4788 = torch.operator "onnx.Unsqueeze"(%4748, %4787) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4789 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2816> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4790 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2817> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4791 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2818> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4792 = torch.operator "onnx.Unsqueeze"(%4754, %4791) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4793 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2819> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4794 = torch.operator "onnx.Unsqueeze"(%4757, %4793) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4795 = torch.operator "onnx.Concat"(%4788, %4789, %4790, %4792, %4794) {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> 
    %4796 = torch.operator "onnx.Reshape"(%4023, %4795) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,12,?,?],f32>, !torch.vtensor<[5],si64>) -> !torch.vtensor<[2,?,4,?,?],f32> 
    %4797 = torch.operator "onnx.Transpose"(%4796) {torch.onnx.perm = [0 : si64, 3 : si64, 4 : si64, 1 : si64, 2 : si64]} : (!torch.vtensor<[2,?,4,?,?],f32>) -> !torch.vtensor<[2,?,?,?,4],f32> 
    %4798 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2820> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4799 = torch.operator "onnx.Unsqueeze"(%4748, %4798) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4800 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2821> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4801 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2822> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4802 = torch.operator "onnx.Concat"(%4799, %4800, %4801) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %4803 = torch.operator "onnx.Reshape"(%4797, %4802) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,?,?,?,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,?,4],f32> 
    %4804 = torch.operator "onnx.Concat"(%4554, %4612, %4670, %4728, %4786) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> 
    %4805 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2823> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4806 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2824> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4807 = torch.operator "onnx.QuantizeLinear"(%4804, %4805, %4806) : (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?,?],si8> 
    %4808 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2825> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4809 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2826> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4810 = torch.operator "onnx.DequantizeLinear"(%4807, %4808, %4809) : (!torch.vtensor<[?,?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?,?],f32> 
    %4811 = torch.operator "onnx.Flatten"(%4810) {torch.onnx.axis = -1 : si64} : (!torch.vtensor<[?,?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %4812 = torch.operator "onnx.Concat"(%4571, %4629, %4687, %4745, %4803) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[2,?,4],f32>, !torch.vtensor<[2,?,4],f32>, !torch.vtensor<[2,?,4],f32>, !torch.vtensor<[2,?,4],f32>, !torch.vtensor<[2,?,4],f32>) -> !torch.vtensor<[2,?,4],f32> 
    %4813 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2827> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4814 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2828> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4815 = torch.operator "onnx.QuantizeLinear"(%4812, %4813, %4814) : (!torch.vtensor<[2,?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,?,4],si8> 
    %4816 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2829> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4817 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2830> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4818 = torch.operator "onnx.DequantizeLinear"(%4815, %4816, %4817) : (!torch.vtensor<[2,?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[2,?,4],f32> 
    %4819 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2831> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %4820 = torch.operator "onnx.Reshape"(%4818, %4819) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[2,?,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %4821 = torch.operator "onnx.Shape"(%4446) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %4822 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2832> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4823 = torch.operator "onnx.Gather"(%4821, %4822) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4824 = torch.operator "onnx.Shape"(%4453) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %4825 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2833> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4826 = torch.operator "onnx.Gather"(%4824, %4825) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4827 = torch.operator "onnx.Concat"(%4446, %4453) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>) -> !torch.vtensor<[?,4],f32> 
    %4828 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2834> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4829 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2835> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4830 = torch.operator "onnx.QuantizeLinear"(%4827, %4828, %4829) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],si8> 
    %4831 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2836> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4832 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2837> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4833 = torch.operator "onnx.DequantizeLinear"(%4830, %4831, %4832) : (!torch.vtensor<[?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],f32> 
    %4834 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2838> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %4835 = torch.operator "onnx.Add"(%4823, %4834) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4836 = torch.operator "onnx.Add"(%4835, %4826) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %4837 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2839> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4838 = torch.operator "onnx.Unsqueeze"(%4836, %4837) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %4839 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2840> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4840 = torch.operator "onnx.Concat"(%4838, %4839) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %4841 = torch.operator "onnx.Reshape"(%4820, %4840) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],f32> 
    %4842 = torch.operator "onnx.Cast"(%4833) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[?,4],f32> 
    %4843 = torch.operator "onnx.Gather"(%4842, %283) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %4844 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2841> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4845 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2842> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4846 = torch.operator "onnx.QuantizeLinear"(%4843, %4844, %4845) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4847 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2843> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4848 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2844> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4849 = torch.operator "onnx.DequantizeLinear"(%4846, %4847, %4848) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4850 = torch.operator "onnx.Gather"(%4842, %284) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %4851 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2845> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4852 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2846> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4853 = torch.operator "onnx.QuantizeLinear"(%4850, %4851, %4852) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4854 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2847> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4855 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2848> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4856 = torch.operator "onnx.DequantizeLinear"(%4853, %4854, %4855) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4857 = torch.operator "onnx.Sub"(%4849, %4856) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %4858 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2849> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4859 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2850> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4860 = torch.operator "onnx.QuantizeLinear"(%4857, %4858, %4859) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4861 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2851> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4862 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2852> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4863 = torch.operator "onnx.DequantizeLinear"(%4860, %4861, %4862) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4864 = torch.operator "onnx.Gather"(%4842, %278) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %4865 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2853> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4866 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2854> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4867 = torch.operator "onnx.QuantizeLinear"(%4864, %4865, %4866) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4868 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2855> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4869 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2856> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4870 = torch.operator "onnx.DequantizeLinear"(%4867, %4868, %4869) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4871 = torch.operator "onnx.Gather"(%4842, %282) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %4872 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2857> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4873 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2858> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4874 = torch.operator "onnx.QuantizeLinear"(%4871, %4872, %4873) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4875 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2859> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4876 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2860> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4877 = torch.operator "onnx.DequantizeLinear"(%4874, %4875, %4876) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4878 = torch.operator "onnx.Sub"(%4870, %4877) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %4879 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2861> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4880 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2862> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4881 = torch.operator "onnx.QuantizeLinear"(%4878, %4879, %4880) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4882 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2863> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4883 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2864> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4884 = torch.operator "onnx.DequantizeLinear"(%4881, %4882, %4883) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4885 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2865> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4886 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2866> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4887 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2867> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4888 = torch.operator "onnx.QuantizeLinear"(%4885, %4886, %4887) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %4889 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2868> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4890 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2869> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4891 = torch.operator "onnx.DequantizeLinear"(%4888, %4889, %4890) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %4892 = torch.operator "onnx.Mul"(%4863, %4891) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
    %4893 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2870> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4894 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2871> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4895 = torch.operator "onnx.QuantizeLinear"(%4892, %4893, %4894) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4896 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2872> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4897 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2873> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4898 = torch.operator "onnx.DequantizeLinear"(%4895, %4896, %4897) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4899 = torch.operator "onnx.Add"(%4856, %4898) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %4900 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2874> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4901 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2875> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4902 = torch.operator "onnx.QuantizeLinear"(%4899, %4900, %4901) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4903 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2876> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4904 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2877> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4905 = torch.operator "onnx.DequantizeLinear"(%4902, %4903, %4904) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4906 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2878> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4907 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2879> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4908 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2880> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4909 = torch.operator "onnx.QuantizeLinear"(%4906, %4907, %4908) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %4910 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2881> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4911 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2882> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4912 = torch.operator "onnx.DequantizeLinear"(%4909, %4910, %4911) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %4913 = torch.operator "onnx.Mul"(%4884, %4912) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
    %4914 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2883> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4915 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2884> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4916 = torch.operator "onnx.QuantizeLinear"(%4913, %4914, %4915) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4917 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2885> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4918 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2886> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4919 = torch.operator "onnx.DequantizeLinear"(%4916, %4917, %4918) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4920 = torch.operator "onnx.Add"(%4877, %4919) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %4921 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2887> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4922 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2888> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4923 = torch.operator "onnx.QuantizeLinear"(%4920, %4921, %4922) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %4924 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2889> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4925 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2890> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4926 = torch.operator "onnx.DequantizeLinear"(%4923, %4924, %4925) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %4927 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2891> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4928 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2892> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4929 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2893> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4930 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2894> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4931 = torch.operator "onnx.Slice"(%4841, %4928, %4929, %4927, %4930) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %4932 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2895> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4933 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2896> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4934 = torch.operator "onnx.QuantizeLinear"(%4931, %4932, %4933) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %4935 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2897> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4936 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2898> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4937 = torch.operator "onnx.DequantizeLinear"(%4934, %4935, %4936) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %4938 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2899> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4939 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2900> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4940 = torch.operator "onnx.QuantizeLinear"(%3, %4938, %4939) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %4941 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2901> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4942 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2902> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4943 = torch.operator "onnx.DequantizeLinear"(%4940, %4941, %4942) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %4944 = torch.operator "onnx.Div"(%4937, %4943) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?],f32> 
    %4945 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2903> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4946 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2904> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4947 = torch.operator "onnx.QuantizeLinear"(%4944, %4945, %4946) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %4948 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2905> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4949 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2906> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4950 = torch.operator "onnx.DequantizeLinear"(%4947, %4948, %4949) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %4951 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2907> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4952 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2908> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4953 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2909> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4954 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2910> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4955 = torch.operator "onnx.Slice"(%4841, %4952, %4953, %4951, %4954) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %4956 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2911> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4957 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2912> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4958 = torch.operator "onnx.QuantizeLinear"(%4955, %4956, %4957) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %4959 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2913> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4960 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2914> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4961 = torch.operator "onnx.DequantizeLinear"(%4958, %4959, %4960) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %4962 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2915> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4963 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2916> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4964 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2917> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4965 = torch.operator "onnx.QuantizeLinear"(%4962, %4963, %4964) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %4966 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2918> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4967 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2919> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4968 = torch.operator "onnx.DequantizeLinear"(%4965, %4966, %4967) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %4969 = torch.operator "onnx.Div"(%4961, %4968) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?],f32> 
    %4970 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2920> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4971 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2921> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4972 = torch.operator "onnx.QuantizeLinear"(%4969, %4970, %4971) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %4973 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2922> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4974 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2923> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4975 = torch.operator "onnx.DequantizeLinear"(%4972, %4973, %4974) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %4976 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2924> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4977 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2925> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4978 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2926> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4979 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2927> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %4980 = torch.operator "onnx.Slice"(%4841, %4977, %4978, %4976, %4979) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %4981 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2928> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4982 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2929> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4983 = torch.operator "onnx.QuantizeLinear"(%4980, %4981, %4982) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %4984 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2930> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4985 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2931> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4986 = torch.operator "onnx.DequantizeLinear"(%4983, %4984, %4985) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %4987 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2932> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4988 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2933> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4989 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2934> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4990 = torch.operator "onnx.QuantizeLinear"(%4987, %4988, %4989) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %4991 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2935> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4992 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2936> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4993 = torch.operator "onnx.DequantizeLinear"(%4990, %4991, %4992) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %4994 = torch.operator "onnx.Div"(%4986, %4993) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?],f32> 
    %4995 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2937> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4996 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2938> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %4997 = torch.operator "onnx.QuantizeLinear"(%4994, %4995, %4996) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %4998 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2939> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %4999 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2940> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5000 = torch.operator "onnx.DequantizeLinear"(%4997, %4998, %4999) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5001 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2941> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5002 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2942> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5003 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2943> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5004 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2944> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5005 = torch.operator "onnx.Slice"(%4841, %5002, %5003, %5001, %5004) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %5006 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2945> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5007 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2946> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5008 = torch.operator "onnx.QuantizeLinear"(%5005, %5006, %5007) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5009 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2947> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5010 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2948> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5011 = torch.operator "onnx.DequantizeLinear"(%5008, %5009, %5010) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5012 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2949> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5013 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2950> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5014 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2951> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5015 = torch.operator "onnx.QuantizeLinear"(%5012, %5013, %5014) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %5016 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2952> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5017 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2953> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5018 = torch.operator "onnx.DequantizeLinear"(%5015, %5016, %5017) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %5019 = torch.operator "onnx.Div"(%5011, %5018) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?],f32> 
    %5020 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2954> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5021 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2955> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5022 = torch.operator "onnx.QuantizeLinear"(%5019, %5020, %5021) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5023 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2956> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5024 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2957> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5025 = torch.operator "onnx.DequantizeLinear"(%5022, %5023, %5024) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5026 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2958> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5027 = torch.operator "onnx.Clip"(%5000, %none, %5026) : (!torch.vtensor<[?,?],f32>, !torch.none, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?],f32> 
    %5028 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2959> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5029 = torch.operator "onnx.Clip"(%5025, %none, %5028) : (!torch.vtensor<[?,?],f32>, !torch.none, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,?],f32> 
    %5030 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2960> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5031 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2961> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5032 = torch.operator "onnx.QuantizeLinear"(%4863, %5030, %5031) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5033 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2962> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5034 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2963> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5035 = torch.operator "onnx.DequantizeLinear"(%5032, %5033, %5034) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5036 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2964> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5037 = torch.operator "onnx.Unsqueeze"(%5035, %5036) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5038 = torch.operator "onnx.Mul"(%4950, %5037) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,?],f32> 
    %5039 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2965> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5040 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2966> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5041 = torch.operator "onnx.QuantizeLinear"(%5038, %5039, %5040) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5042 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2967> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5043 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2968> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5044 = torch.operator "onnx.DequantizeLinear"(%5041, %5042, %5043) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5045 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2969> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5046 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2970> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5047 = torch.operator "onnx.QuantizeLinear"(%4905, %5045, %5046) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5048 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2971> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5049 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2972> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5050 = torch.operator "onnx.DequantizeLinear"(%5047, %5048, %5049) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5051 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2973> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5052 = torch.operator "onnx.Unsqueeze"(%5050, %5051) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5053 = torch.operator "onnx.Add"(%5044, %5052) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,?],f32> 
    %5054 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2974> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5055 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2975> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5056 = torch.operator "onnx.QuantizeLinear"(%5053, %5054, %5055) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5057 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2976> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5058 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2977> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5059 = torch.operator "onnx.DequantizeLinear"(%5056, %5057, %5058) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5060 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2978> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5061 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2979> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5062 = torch.operator "onnx.QuantizeLinear"(%4884, %5060, %5061) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5063 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2980> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5064 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2981> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5065 = torch.operator "onnx.DequantizeLinear"(%5062, %5063, %5064) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5066 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2982> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5067 = torch.operator "onnx.Unsqueeze"(%5065, %5066) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5068 = torch.operator "onnx.Mul"(%4975, %5067) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,?],f32> 
    %5069 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2983> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5070 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2984> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5071 = torch.operator "onnx.QuantizeLinear"(%5068, %5069, %5070) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5072 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2985> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5073 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2986> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5074 = torch.operator "onnx.DequantizeLinear"(%5071, %5072, %5073) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5075 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2987> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5076 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2988> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5077 = torch.operator "onnx.QuantizeLinear"(%4926, %5075, %5076) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5078 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2989> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5079 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2990> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5080 = torch.operator "onnx.DequantizeLinear"(%5077, %5078, %5079) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5081 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2991> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5082 = torch.operator "onnx.Unsqueeze"(%5080, %5081) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5083 = torch.operator "onnx.Add"(%5074, %5082) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,?],f32> 
    %5084 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2992> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5085 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2993> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5086 = torch.operator "onnx.QuantizeLinear"(%5083, %5084, %5085) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5087 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2994> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5088 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2995> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5089 = torch.operator "onnx.DequantizeLinear"(%5086, %5087, %5088) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5090 = torch.operator "onnx.Exp"(%5027) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5091 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2996> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5092 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2997> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5093 = torch.operator "onnx.QuantizeLinear"(%5090, %5091, %5092) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5094 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2998> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5095 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__2999> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5096 = torch.operator "onnx.DequantizeLinear"(%5093, %5094, %5095) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5097 = torch.operator "onnx.Mul"(%5096, %5037) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,?],f32> 
    %5098 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3000> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5099 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3001> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5100 = torch.operator "onnx.QuantizeLinear"(%5097, %5098, %5099) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5101 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3002> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5102 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3003> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5103 = torch.operator "onnx.DequantizeLinear"(%5100, %5101, %5102) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5104 = torch.operator "onnx.Exp"(%5029) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5105 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3004> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5106 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3005> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5107 = torch.operator "onnx.QuantizeLinear"(%5104, %5105, %5106) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5108 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3006> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5109 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3007> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5110 = torch.operator "onnx.DequantizeLinear"(%5107, %5108, %5109) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5111 = torch.operator "onnx.Mul"(%5110, %5067) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,?],f32> 
    %5112 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3008> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5113 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3009> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5114 = torch.operator "onnx.QuantizeLinear"(%5111, %5112, %5113) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5115 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3010> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5116 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3011> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5117 = torch.operator "onnx.DequantizeLinear"(%5114, %5115, %5116) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5118 = torch.operator "onnx.Mul"(%302, %5117) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5119 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3012> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5120 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3013> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5121 = torch.operator "onnx.QuantizeLinear"(%5118, %5119, %5120) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5122 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3014> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5123 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3015> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5124 = torch.operator "onnx.DequantizeLinear"(%5121, %5122, %5123) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5125 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3016> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5126 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3017> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5127 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3018> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5128 = torch.operator "onnx.QuantizeLinear"(%5125, %5126, %5127) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %5129 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3019> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5130 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3020> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5131 = torch.operator "onnx.DequantizeLinear"(%5128, %5129, %5130) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %5132 = torch.operator "onnx.Mul"(%5131, %5103) : (!torch.vtensor<[],f32>, !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5133 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3021> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5134 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3022> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5135 = torch.operator "onnx.QuantizeLinear"(%5132, %5133, %5134) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5136 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3023> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5137 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3024> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5138 = torch.operator "onnx.DequantizeLinear"(%5135, %5136, %5137) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5139 = torch.operator "onnx.Sub"(%5059, %5138) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5140 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3025> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5141 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3026> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5142 = torch.operator "onnx.QuantizeLinear"(%5139, %5140, %5141) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5143 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3027> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5144 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3028> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5145 = torch.operator "onnx.DequantizeLinear"(%5142, %5143, %5144) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5146 = torch.operator "onnx.Sub"(%5089, %5124) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5147 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3029> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5148 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3030> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5149 = torch.operator "onnx.QuantizeLinear"(%5146, %5147, %5148) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5150 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3031> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5151 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3032> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5152 = torch.operator "onnx.DequantizeLinear"(%5149, %5150, %5151) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5153 = torch.operator "onnx.Add"(%5059, %5138) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5154 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3033> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5155 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3034> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5156 = torch.operator "onnx.QuantizeLinear"(%5153, %5154, %5155) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5157 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3035> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5158 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3036> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5159 = torch.operator "onnx.DequantizeLinear"(%5156, %5157, %5158) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5160 = torch.operator "onnx.Add"(%5089, %5124) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5161 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3037> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5162 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3038> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5163 = torch.operator "onnx.QuantizeLinear"(%5160, %5161, %5162) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],si8> 
    %5164 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3039> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5165 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3040> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5166 = torch.operator "onnx.DequantizeLinear"(%5163, %5164, %5165) : (!torch.vtensor<[?,?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,?],f32> 
    %5167 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3041> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5168 = torch.operator "onnx.Unsqueeze"(%5145, %5167) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?,1],f32> 
    %5169 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3042> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5170 = torch.operator "onnx.Unsqueeze"(%5152, %5169) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?,1],f32> 
    %5171 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3043> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5172 = torch.operator "onnx.Unsqueeze"(%5159, %5171) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?,1],f32> 
    %5173 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3044> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5174 = torch.operator "onnx.Unsqueeze"(%5166, %5173) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?,1],f32> 
    %5175 = torch.operator "onnx.Concat"(%5168, %5170, %5172, %5174) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[?,?,1],f32>, !torch.vtensor<[?,?,1],f32>, !torch.vtensor<[?,?,1],f32>, !torch.vtensor<[?,?,1],f32>) -> !torch.vtensor<[?,?,4],f32> 
    %5176 = torch.operator "onnx.Flatten"(%5175) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,?,4],f32>) -> !torch.vtensor<[?,?],f32> 
    %5177 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3045> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5178 = torch.operator "onnx.Unsqueeze"(%4836, %5177) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5179 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3046> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5180 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3047> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5181 = torch.operator "onnx.Concat"(%5178, %5179, %5180) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %5182 = torch.operator "onnx.Reshape"(%5176, %5181) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,4],f32> 
    %5183 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3048> : tensor<3xsi64>} : () -> !torch.vtensor<[3],si64> 
    %5184 = torch.operator "onnx.Reshape"(%5182, %5183) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,?,4],f32> 
    %5185 = torch.operator "onnx.Shape"(%5184) : (!torch.vtensor<[2,?,4],f32>) -> !torch.vtensor<[3],si64> 
    %5186 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3049> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5187 = torch.operator "onnx.Gather"(%5185, %5186) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5188 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3050> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5189 = torch.operator "onnx.Unsqueeze"(%5187, %5188) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5190 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3051> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5191 = torch.operator "onnx.Concat"(%5189, %5190) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5192 = torch.operator "onnx.Reshape"(%4811, %5191) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[2,?],f32> 
    %5193 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3052> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5194 = torch.operator "onnx.Unsqueeze"(%4505, %5193) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5195 = torch.operator "onnx.Concat"(%5194) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5196 = torch.operator "onnx.ConstantOfShape"(%5195) {torch.onnx.value = dense_resource<__3053> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5197 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3054> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5198 = torch.operator "onnx.Unsqueeze"(%4507, %5197) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5199 = torch.operator "onnx.Concat"(%5198) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5200 = torch.operator "onnx.ConstantOfShape"(%5199) {torch.onnx.value = dense_resource<__3055> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5201 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3056> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5202 = torch.operator "onnx.Unsqueeze"(%4509, %5201) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5203 = torch.operator "onnx.Concat"(%5202) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5204 = torch.operator "onnx.ConstantOfShape"(%5203) {torch.onnx.value = dense_resource<__3057> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5205 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3058> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5206 = torch.operator "onnx.Unsqueeze"(%4511, %5205) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5207 = torch.operator "onnx.Concat"(%5206) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5208 = torch.operator "onnx.ConstantOfShape"(%5207) {torch.onnx.value = dense_resource<__3059> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5209 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3060> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5210 = torch.operator "onnx.Unsqueeze"(%4513, %5209) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5211 = torch.operator "onnx.Concat"(%5210) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5212 = torch.operator "onnx.ConstantOfShape"(%5211) {torch.onnx.value = dense_resource<__3061> : tensor<1xsi64>} : (!torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5213 = torch.operator "onnx.Concat"(%5196, %5200, %5204, %5208, %5212) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],si64> 
    %5214 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3062> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %5215 = torch.operator "onnx.Reshape"(%5213, %5214) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[1,?],si64> 
    %5216 = torch.operator "onnx.Shape"(%5192) : (!torch.vtensor<[2,?],f32>) -> !torch.vtensor<[2],si64> 
    %5217 = torch.operator "onnx.Expand"(%5215, %5216) : (!torch.vtensor<[1,?],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[2,?],si64> 
    %5218 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3063> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5219 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3064> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5220 = torch.operator "onnx.Unsqueeze"(%4505, %5219) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5221 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3065> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5222 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3066> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5223 = torch.operator "onnx.Slice"(%5192, %5218, %5220, %5221, %5222) : (!torch.vtensor<[2,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %5224 = torch.operator "onnx.Add"(%4505, %4507) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5225 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3067> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5226 = torch.operator "onnx.Unsqueeze"(%4505, %5225) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5227 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3068> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5228 = torch.operator "onnx.Unsqueeze"(%5224, %5227) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5229 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3069> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5230 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3070> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5231 = torch.operator "onnx.Slice"(%5192, %5226, %5228, %5229, %5230) : (!torch.vtensor<[2,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %5232 = torch.operator "onnx.Add"(%5224, %4509) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5233 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3071> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5234 = torch.operator "onnx.Unsqueeze"(%5224, %5233) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5235 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3072> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5236 = torch.operator "onnx.Unsqueeze"(%5232, %5235) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5237 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3073> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5238 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3074> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5239 = torch.operator "onnx.Slice"(%5192, %5234, %5236, %5237, %5238) : (!torch.vtensor<[2,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %5240 = torch.operator "onnx.Add"(%5232, %4511) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5241 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3075> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5242 = torch.operator "onnx.Unsqueeze"(%5232, %5241) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5243 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3076> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5244 = torch.operator "onnx.Unsqueeze"(%5240, %5243) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5245 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3077> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5246 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3078> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5247 = torch.operator "onnx.Slice"(%5192, %5242, %5244, %5245, %5246) : (!torch.vtensor<[2,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %5248 = torch.operator "onnx.Add"(%5240, %4513) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5249 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3079> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5250 = torch.operator "onnx.Unsqueeze"(%5240, %5249) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5251 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3080> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5252 = torch.operator "onnx.Unsqueeze"(%5248, %5251) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5253 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3081> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5254 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3082> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5255 = torch.operator "onnx.Slice"(%5192, %5250, %5252, %5253, %5254) : (!torch.vtensor<[2,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,?],f32> 
    %5256 = torch.operator "onnx.Shape"(%5223) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5257 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3083> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5258 = torch.operator "onnx.Gather"(%5256, %5257) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5259 = torch.operator "onnx.Shape"(%5223) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5260 = torch.operator "onnx.Gather"(%5259, %282) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5261 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3084> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5262 = torch.operator "onnx.Unsqueeze"(%5260, %5261) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5263 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3085> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5264 = torch.operator "onnx.Concat"(%5263, %5262) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5265 = torch.operator "onnx.ReduceMin"(%5264) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %5266 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3086> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5267 = torch.operator "onnx.Reshape"(%5265, %5266) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5268:2 = torch.operator "onnx.TopK"(%5223, %5267) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 1 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],si64>) 
    %5269 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3087> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5270 = torch.operator "onnx.Add"(%5258, %5269) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5271 = torch.operator "onnx.Shape"(%5231) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5272 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3088> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5273 = torch.operator "onnx.Gather"(%5271, %5272) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5274 = torch.operator "onnx.Shape"(%5231) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5275 = torch.operator "onnx.Gather"(%5274, %282) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5276 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3089> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5277 = torch.operator "onnx.Unsqueeze"(%5275, %5276) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5278 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3090> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5279 = torch.operator "onnx.Concat"(%5278, %5277) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5280 = torch.operator "onnx.ReduceMin"(%5279) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %5281 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3091> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5282 = torch.operator "onnx.Reshape"(%5280, %5281) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5283:2 = torch.operator "onnx.TopK"(%5231, %5282) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 1 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],si64>) 
    %5284 = torch.operator "onnx.Add"(%5283#1, %5270) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,?],si64> 
    %5285 = torch.operator "onnx.Add"(%5270, %5273) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5286 = torch.operator "onnx.Shape"(%5239) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5287 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3092> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5288 = torch.operator "onnx.Gather"(%5286, %5287) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5289 = torch.operator "onnx.Shape"(%5239) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5290 = torch.operator "onnx.Gather"(%5289, %282) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5291 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3093> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5292 = torch.operator "onnx.Unsqueeze"(%5290, %5291) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5293 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3094> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5294 = torch.operator "onnx.Concat"(%5293, %5292) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5295 = torch.operator "onnx.ReduceMin"(%5294) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %5296 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3095> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5297 = torch.operator "onnx.Reshape"(%5295, %5296) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5298:2 = torch.operator "onnx.TopK"(%5239, %5297) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 1 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],si64>) 
    %5299 = torch.operator "onnx.Add"(%5298#1, %5285) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,?],si64> 
    %5300 = torch.operator "onnx.Add"(%5285, %5288) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5301 = torch.operator "onnx.Shape"(%5247) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5302 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3096> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5303 = torch.operator "onnx.Gather"(%5301, %5302) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5304 = torch.operator "onnx.Shape"(%5247) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5305 = torch.operator "onnx.Gather"(%5304, %282) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5306 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3097> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5307 = torch.operator "onnx.Unsqueeze"(%5305, %5306) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5308 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3098> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5309 = torch.operator "onnx.Concat"(%5308, %5307) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5310 = torch.operator "onnx.ReduceMin"(%5309) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %5311 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3099> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5312 = torch.operator "onnx.Reshape"(%5310, %5311) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5313:2 = torch.operator "onnx.TopK"(%5247, %5312) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 1 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],si64>) 
    %5314 = torch.operator "onnx.Add"(%5313#1, %5300) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,?],si64> 
    %5315 = torch.operator "onnx.Add"(%5300, %5303) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5316 = torch.operator "onnx.Shape"(%5255) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[2],si64> 
    %5317 = torch.operator "onnx.Gather"(%5316, %282) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5318 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3100> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5319 = torch.operator "onnx.Unsqueeze"(%5317, %5318) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5320 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3101> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5321 = torch.operator "onnx.Concat"(%5320, %5319) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5322 = torch.operator "onnx.ReduceMin"(%5321) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %5323 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3102> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5324 = torch.operator "onnx.Reshape"(%5322, %5323) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5325:2 = torch.operator "onnx.TopK"(%5255, %5324) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 1 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],si64>) 
    %5326 = torch.operator "onnx.Add"(%5325#1, %5315) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,?],si64> 
    %5327 = torch.operator "onnx.Concat"(%5268#1, %5284, %5299, %5314, %5326) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[?,?],si64>, !torch.vtensor<[?,?],si64>, !torch.vtensor<[?,?],si64>, !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?],si64> 
    %5328 = torch.operator "onnx.Cast"(%5187) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5329 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3103> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5330 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3104> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5331 = torch.operator "onnx.Range"(%5329, %5328, %5330) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],si64> 
    %5332 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3105> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5333 = torch.operator "onnx.Unsqueeze"(%5331, %5332) : (!torch.vtensor<[?],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5334 = torch.operator "onnx.Shape"(%5192) : (!torch.vtensor<[2,?],f32>) -> !torch.vtensor<[2],si64> 
    %5335 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3106> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5336 = torch.operator "onnx.Gather"(%5334, %5335) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5337 = torch.operator "onnx.Flatten"(%5192) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[2,?],f32>) -> !torch.vtensor<[?,1],f32> 
    %5338 = torch.operator "onnx.Mul"(%5333, %5336) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5339 = torch.operator "onnx.Add"(%5327, %5338) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[?,1],si64>) -> !torch.vtensor<[?,?],si64> 
    %5340 = torch.operator "onnx.Gather"(%5337, %5339) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,1],f32>, !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?,1],f32> 
    %5341 = torch.operator "onnx.Shape"(%5339) : (!torch.vtensor<[?,?],si64>) -> !torch.vtensor<[2],si64> 
    %5342 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3107> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5343 = torch.operator "onnx.Reshape"(%5340, %5342) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,1],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],f32> 
    %5344 = torch.operator "onnx.Concat"(%5341) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],si64> 
    %5345 = torch.operator "onnx.Reshape"(%5343, %5344) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],f32> 
    %5346 = torch.operator "onnx.Shape"(%5217) : (!torch.vtensor<[2,?],si64>) -> !torch.vtensor<[2],si64> 
    %5347 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3108> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5348 = torch.operator "onnx.Gather"(%5346, %5347) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5349 = torch.operator "onnx.Flatten"(%5217) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[2,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5350 = torch.operator "onnx.Mul"(%5333, %5348) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5351 = torch.operator "onnx.Add"(%5327, %5350) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[?,1],si64>) -> !torch.vtensor<[?,?],si64> 
    %5352 = torch.operator "onnx.Gather"(%5349, %5351) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?,1],si64> 
    %5353 = torch.operator "onnx.Shape"(%5351) : (!torch.vtensor<[?,?],si64>) -> !torch.vtensor<[2],si64> 
    %5354 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3109> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5355 = torch.operator "onnx.Reshape"(%5352, %5354) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5356 = torch.operator "onnx.Concat"(%5353) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[2],si64> 
    %5357 = torch.operator "onnx.Reshape"(%5355, %5356) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?],si64>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,?],si64> 
    %5358 = torch.operator "onnx.Shape"(%5184) : (!torch.vtensor<[2,?,4],f32>) -> !torch.vtensor<[3],si64> 
    %5359 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3110> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5360 = torch.operator "onnx.Gather"(%5358, %5359) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5361 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3111> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5362 = torch.operator "onnx.Gather"(%5358, %5361) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5363 = torch.operator "onnx.Flatten"(%5184) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[2,?,4],f32>) -> !torch.vtensor<[?,4],f32> 
    %5364 = torch.operator "onnx.Mul"(%5333, %5360) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5365 = torch.operator "onnx.Add"(%5327, %5364) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[?,1],si64>) -> !torch.vtensor<[?,?],si64> 
    %5366 = torch.operator "onnx.Gather"(%5363, %5365) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,?],si64>) -> !torch.vtensor<[?,?,4],f32> 
    %5367 = torch.operator "onnx.Shape"(%5365) : (!torch.vtensor<[?,?],si64>) -> !torch.vtensor<[2],si64> 
    %5368 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3112> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5369 = torch.operator "onnx.Concat"(%5368, %5362) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5370 = torch.operator "onnx.Reshape"(%5366, %5369) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,?,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %5371 = torch.operator "onnx.Concat"(%5367, %5362) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3],si64> 
    %5372 = torch.operator "onnx.Reshape"(%5370, %5371) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,4],f32> 
    %5373 = torch.operator "onnx.Sigmoid"(%5345) : (!torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> 
    %5374 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3113> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %5375:2 = torch.operator "onnx.Split"(%5372, %5374) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,?,4],f32>, !torch.vtensor<[2],si64>) -> (!torch.vtensor<[?,?,4],f32>, !torch.vtensor<[?,?,4],f32>) 
    %5376 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3114> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5377 = torch.operator "onnx.Squeeze"(%5375#0, %5376) : (!torch.vtensor<[?,?,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,4],f32> 
    %5378 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3115> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5379 = torch.operator "onnx.Squeeze"(%5375#1, %5378) : (!torch.vtensor<[?,?,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,4],f32> 
    %5380 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3116> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %5381:2 = torch.operator "onnx.Split"(%5373, %5380) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[2],si64>) -> (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) 
    %5382 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3117> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5383 = torch.operator "onnx.Squeeze"(%5381#0, %5382) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],f32> 
    %5384 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3118> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5385 = torch.operator "onnx.Squeeze"(%5381#1, %5384) : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],f32> 
    %5386 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3119> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %5387:2 = torch.operator "onnx.Split"(%5357, %5386) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[2],si64>) -> (!torch.vtensor<[?,?],si64>, !torch.vtensor<[?,?],si64>) 
    %5388 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3120> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5389 = torch.operator "onnx.Squeeze"(%5387#0, %5388) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5390 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3121> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5391 = torch.operator "onnx.Squeeze"(%5387#1, %5390) : (!torch.vtensor<[?,?],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5392 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3122> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5393 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3123> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5394 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3124> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5395 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3125> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5396 = torch.operator "onnx.Slice"(%5377, %5393, %5394, %5392, %5395) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2],f32> 
    %5397 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3126> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5398 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3127> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5399 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3128> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5400 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3129> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5401 = torch.operator "onnx.Slice"(%5377, %5398, %5399, %5397, %5400) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2],f32> 
    %5402 = torch.operator "onnx.Max"(%5396, %281) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5403 = torch.operator "onnx.Cast"(%1184) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %5404 = torch.operator "onnx.Min"(%5402, %5403) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5405 = torch.operator "onnx.Max"(%5401, %281) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5406 = torch.operator "onnx.Cast"(%1181) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %5407 = torch.operator "onnx.Min"(%5405, %5406) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5408 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3130> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5409 = torch.operator "onnx.Unsqueeze"(%5404, %5408) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2,1],f32> 
    %5410 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3131> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5411 = torch.operator "onnx.Unsqueeze"(%5407, %5410) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2,1],f32> 
    %5412 = torch.operator "onnx.Concat"(%5409, %5411) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[?,2,1],f32>, !torch.vtensor<[?,2,1],f32>) -> !torch.vtensor<[?,2,2],f32> 
    %5413 = torch.operator "onnx.Shape"(%5377) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %5414 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3132> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5415 = torch.operator "onnx.Gather"(%5413, %5414) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5416 = torch.operator "onnx.Shape"(%5377) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %5417 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3133> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5418 = torch.operator "onnx.Gather"(%5416, %5417) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5419 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3134> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5420 = torch.operator "onnx.Unsqueeze"(%5415, %5419) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5421 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3135> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5422 = torch.operator "onnx.Unsqueeze"(%5418, %5421) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5423 = torch.operator "onnx.Concat"(%5420, %5422) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5424 = torch.operator "onnx.Reshape"(%5412, %5423) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,2,2],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %5425 = torch.operator "onnx.Gather"(%5424, %283) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5426 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3136> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5427 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3137> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5428 = torch.operator "onnx.QuantizeLinear"(%5425, %5426, %5427) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5429 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3138> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5430 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3139> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5431 = torch.operator "onnx.DequantizeLinear"(%5428, %5429, %5430) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5432 = torch.operator "onnx.Gather"(%5424, %284) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5433 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3140> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5434 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3141> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5435 = torch.operator "onnx.QuantizeLinear"(%5432, %5433, %5434) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5436 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3142> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5437 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3143> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5438 = torch.operator "onnx.DequantizeLinear"(%5435, %5436, %5437) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5439 = torch.operator "onnx.Sub"(%5431, %5438) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5440 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3144> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5441 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3145> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5442 = torch.operator "onnx.QuantizeLinear"(%5439, %5440, %5441) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5443 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3146> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5444 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3147> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5445 = torch.operator "onnx.DequantizeLinear"(%5442, %5443, %5444) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5446 = torch.operator "onnx.Gather"(%5424, %278) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5447 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3148> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5448 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3149> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5449 = torch.operator "onnx.QuantizeLinear"(%5446, %5447, %5448) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5450 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3150> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5451 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3151> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5452 = torch.operator "onnx.DequantizeLinear"(%5449, %5450, %5451) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5453 = torch.operator "onnx.Gather"(%5424, %282) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5454 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3152> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5455 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3153> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5456 = torch.operator "onnx.QuantizeLinear"(%5453, %5454, %5455) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5457 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3154> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5458 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3155> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5459 = torch.operator "onnx.DequantizeLinear"(%5456, %5457, %5458) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5460 = torch.operator "onnx.Sub"(%5452, %5459) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5461 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3156> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5462 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3157> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5463 = torch.operator "onnx.QuantizeLinear"(%5460, %5461, %5462) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5464 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3158> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5465 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3159> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5466 = torch.operator "onnx.DequantizeLinear"(%5463, %5464, %5465) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5467 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3160> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5468 = torch.operator "onnx.GreaterOrEqual"(%5445, %5467) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],i1> 
    %5469 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3161> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5470 = torch.operator "onnx.GreaterOrEqual"(%5466, %5469) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],i1> 
    %5471 = torch.operator "onnx.And"(%5468, %5470) : (!torch.vtensor<[?],i1>, !torch.vtensor<[?],i1>) -> !torch.vtensor<[?],i1> 
    %5472 = torch.operator "onnx.NonZero"(%5471) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5473 = torch.operator "onnx.Transpose"(%5472) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5474 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3162> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5475 = torch.operator "onnx.Split"(%5473, %5474) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5476 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3163> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5477 = torch.operator "onnx.Squeeze"(%5475, %5476) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5478 = torch.operator "onnx.Gather"(%5424, %5477) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,4],f32> 
    %5479 = torch.operator "onnx.Gather"(%5383, %5477) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],f32> 
    %5480 = torch.operator "onnx.Gather"(%5389, %5477) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],si64> 
    %5481 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3164> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5482 = torch.operator "onnx.GreaterOrEqual"(%5479, %5481) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],i1> 
    %5483 = torch.operator "onnx.NonZero"(%5482) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5484 = torch.operator "onnx.Transpose"(%5483) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5485 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3165> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5486 = torch.operator "onnx.Split"(%5484, %5485) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5487 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3166> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5488 = torch.operator "onnx.Squeeze"(%5486, %5487) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5489 = torch.operator "onnx.Gather"(%5478, %5488) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,4],f32> 
    %5490 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3167> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5491 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3168> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5492 = torch.operator "onnx.QuantizeLinear"(%5489, %5490, %5491) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],si8> 
    %5493 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3169> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5494 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3170> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5495 = torch.operator "onnx.DequantizeLinear"(%5492, %5493, %5494) : (!torch.vtensor<[?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],f32> 
    %5496 = torch.operator "onnx.Gather"(%5479, %5488) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],f32> 
    %5497 = torch.operator "onnx.Gather"(%5480, %5488) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],si64> 
    %5498 = torch.operator "onnx.Shape"(%5495) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %5499 = torch.operator "onnx.ReduceProd"(%5498) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %5500 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3171> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5501 = torch.operator "onnx.Equal"(%5499, %5500) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],i1> 
    %5502 = torch.operator "onnx.Cast"(%5501) {torch.onnx.to = 9 : si64} : (!torch.vtensor<[],i1>) -> !torch.vtensor<[],i1> 
    %5503 = torch.operator "onnx.If"(%5502) : (!torch.vtensor<[],i1>) -> !torch.vtensor<[?],si64> {
      %7627 = torch.operator "onnx.ReduceMax"(%5495) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[],f32> 
      %7628 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3172> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7629 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3173> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7630 = torch.operator "onnx.QuantizeLinear"(%7627, %7628, %7629) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
      %7631 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3174> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7632 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3175> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7633 = torch.operator "onnx.DequantizeLinear"(%7630, %7631, %7632) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
      %7634 = torch.operator "onnx.Cast"(%5497) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?],si64>) -> !torch.vtensor<[?],f32> 
      %7635 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3176> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7636 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3177> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7637 = torch.operator "onnx.QuantizeLinear"(%7634, %7635, %7636) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
      %7638 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3178> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7639 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3179> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7640 = torch.operator "onnx.DequantizeLinear"(%7637, %7638, %7639) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
      %7641 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3180> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7642 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3181> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7643 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3182> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7644 = torch.operator "onnx.QuantizeLinear"(%7641, %7642, %7643) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
      %7645 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3183> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7646 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3184> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7647 = torch.operator "onnx.DequantizeLinear"(%7644, %7645, %7646) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
      %7648 = torch.operator "onnx.Add"(%7633, %7647) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
      %7649 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3185> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7650 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3186> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7651 = torch.operator "onnx.QuantizeLinear"(%7648, %7649, %7650) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
      %7652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3187> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7653 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3188> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7654 = torch.operator "onnx.DequantizeLinear"(%7651, %7652, %7653) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
      %7655 = torch.operator "onnx.Mul"(%7640, %7654) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
      %7656 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3189> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7657 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3190> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7658 = torch.operator "onnx.QuantizeLinear"(%7655, %7656, %7657) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
      %7659 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3191> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7660 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3192> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7661 = torch.operator "onnx.DequantizeLinear"(%7658, %7659, %7660) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
      %7662 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3193> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7663 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3194> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7664 = torch.operator "onnx.QuantizeLinear"(%7661, %7662, %7663) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
      %7665 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3195> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3196> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7667 = torch.operator "onnx.DequantizeLinear"(%7664, %7665, %7666) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
      %7668 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3197> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7669 = torch.operator "onnx.Unsqueeze"(%7667, %7668) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
      %7670 = torch.operator "onnx.Add"(%5495, %7669) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,4],f32> 
      %7671 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3198> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7672 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3199> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7673 = torch.operator "onnx.QuantizeLinear"(%7670, %7671, %7672) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],si8> 
      %7674 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3200> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7675 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3201> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7676 = torch.operator "onnx.DequantizeLinear"(%7673, %7674, %7675) : (!torch.vtensor<[?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],f32> 
      %7677 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3202> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7678 = torch.operator "onnx.Unsqueeze"(%7676, %7677) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,?,4],f32> 
      %7679 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3203> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7680 = torch.operator "onnx.Unsqueeze"(%5496, %7679) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,?],f32> 
      %7681 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3204> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7682 = torch.operator "onnx.Unsqueeze"(%7680, %7681) : (!torch.vtensor<[1,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,1,?],f32> 
      %7683 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3205> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7684 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3206> : tensor<1xf32>} : () -> !torch.vtensor<[1],f32> 
      %7685 = torch.operator "onnx.NonMaxSuppression"(%7678, %7682, %7683, %7684) : (!torch.vtensor<[1,?,4],f32>, !torch.vtensor<[1,1,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],f32>) -> !torch.vtensor<[?,3],si64> 
      %7686 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3207> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7687 = torch.operator "onnx.Gather"(%7685, %7686) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,3],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
      %7688 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3208> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7689 = torch.operator "onnx.Squeeze"(%7687, %7688) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
      torch.operator_terminator %7689 : !torch.vtensor<[?],si64>
    }, {
      %7627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3209> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
      torch.operator_terminator %7627 : !torch.vtensor<[0],si64>
    }
    %5504 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3210> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5505 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3211> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5506 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3212> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5507 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3213> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5508 = torch.operator "onnx.Slice"(%5503, %5505, %5506, %5504, %5507) : (!torch.vtensor<[?],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5509 = torch.operator "onnx.Gather"(%5495, %5508) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,4],f32> 
    %5510 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3214> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5511 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3215> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5512 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3216> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5513 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3217> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5514 = torch.operator "onnx.Slice"(%5379, %5511, %5512, %5510, %5513) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2],f32> 
    %5515 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3218> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5516 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3219> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5517 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3220> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5518 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3221> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5519 = torch.operator "onnx.Slice"(%5379, %5516, %5517, %5515, %5518) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2],f32> 
    %5520 = torch.operator "onnx.Max"(%5514, %281) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5521 = torch.operator "onnx.Cast"(%1190) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %5522 = torch.operator "onnx.Min"(%5520, %5521) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5523 = torch.operator "onnx.Max"(%5519, %281) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5524 = torch.operator "onnx.Cast"(%1187) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[],si64>) -> !torch.vtensor<[],f32> 
    %5525 = torch.operator "onnx.Min"(%5523, %5524) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?,2],f32> 
    %5526 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3222> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5527 = torch.operator "onnx.Unsqueeze"(%5522, %5526) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2,1],f32> 
    %5528 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3223> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5529 = torch.operator "onnx.Unsqueeze"(%5525, %5528) : (!torch.vtensor<[?,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,2,1],f32> 
    %5530 = torch.operator "onnx.Concat"(%5527, %5529) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[?,2,1],f32>, !torch.vtensor<[?,2,1],f32>) -> !torch.vtensor<[?,2,2],f32> 
    %5531 = torch.operator "onnx.Shape"(%5379) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %5532 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3224> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5533 = torch.operator "onnx.Gather"(%5531, %5532) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5534 = torch.operator "onnx.Shape"(%5379) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %5535 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3225> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5536 = torch.operator "onnx.Gather"(%5534, %5535) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],si64> 
    %5537 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3226> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5538 = torch.operator "onnx.Unsqueeze"(%5533, %5537) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5539 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3227> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5540 = torch.operator "onnx.Unsqueeze"(%5536, %5539) : (!torch.vtensor<[],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5541 = torch.operator "onnx.Concat"(%5538, %5540) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2],si64> 
    %5542 = torch.operator "onnx.Reshape"(%5530, %5541) {torch.onnx.allowzero = 0 : si64} : (!torch.vtensor<[?,2,2],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,4],f32> 
    %5543 = torch.operator "onnx.Gather"(%5542, %283) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5544 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3228> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5545 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3229> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5546 = torch.operator "onnx.QuantizeLinear"(%5543, %5544, %5545) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5547 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3230> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5548 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3231> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5549 = torch.operator "onnx.DequantizeLinear"(%5546, %5547, %5548) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5550 = torch.operator "onnx.Gather"(%5542, %284) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5551 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3232> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5552 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3233> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5553 = torch.operator "onnx.QuantizeLinear"(%5550, %5551, %5552) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5554 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3234> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5555 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3235> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5556 = torch.operator "onnx.DequantizeLinear"(%5553, %5554, %5555) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5557 = torch.operator "onnx.Sub"(%5549, %5556) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5558 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3236> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5559 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3237> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5560 = torch.operator "onnx.QuantizeLinear"(%5557, %5558, %5559) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5561 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3238> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5562 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3239> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5563 = torch.operator "onnx.DequantizeLinear"(%5560, %5561, %5562) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5564 = torch.operator "onnx.Gather"(%5542, %278) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5565 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3240> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5566 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3241> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5567 = torch.operator "onnx.QuantizeLinear"(%5564, %5565, %5566) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5568 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3242> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5569 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3243> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5570 = torch.operator "onnx.DequantizeLinear"(%5567, %5568, %5569) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5571 = torch.operator "onnx.Gather"(%5542, %282) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5572 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3244> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5573 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3245> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5574 = torch.operator "onnx.QuantizeLinear"(%5571, %5572, %5573) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5575 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3246> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5576 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3247> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5577 = torch.operator "onnx.DequantizeLinear"(%5574, %5575, %5576) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5578 = torch.operator "onnx.Sub"(%5570, %5577) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5579 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3248> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5580 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3249> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5581 = torch.operator "onnx.QuantizeLinear"(%5578, %5579, %5580) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5582 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3250> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5583 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3251> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5584 = torch.operator "onnx.DequantizeLinear"(%5581, %5582, %5583) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5585 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3252> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5586 = torch.operator "onnx.GreaterOrEqual"(%5563, %5585) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],i1> 
    %5587 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3253> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5588 = torch.operator "onnx.GreaterOrEqual"(%5584, %5587) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],i1> 
    %5589 = torch.operator "onnx.And"(%5586, %5588) : (!torch.vtensor<[?],i1>, !torch.vtensor<[?],i1>) -> !torch.vtensor<[?],i1> 
    %5590 = torch.operator "onnx.NonZero"(%5589) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5591 = torch.operator "onnx.Transpose"(%5590) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5592 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3254> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5593 = torch.operator "onnx.Split"(%5591, %5592) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5594 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3255> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5595 = torch.operator "onnx.Squeeze"(%5593, %5594) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5596 = torch.operator "onnx.Gather"(%5542, %5595) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,4],f32> 
    %5597 = torch.operator "onnx.Gather"(%5385, %5595) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],f32> 
    %5598 = torch.operator "onnx.Gather"(%5391, %5595) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],si64> 
    %5599 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3256> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5600 = torch.operator "onnx.GreaterOrEqual"(%5597, %5599) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],i1> 
    %5601 = torch.operator "onnx.NonZero"(%5600) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5602 = torch.operator "onnx.Transpose"(%5601) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5603 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3257> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5604 = torch.operator "onnx.Split"(%5602, %5603) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5605 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3258> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5606 = torch.operator "onnx.Squeeze"(%5604, %5605) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5607 = torch.operator "onnx.Gather"(%5596, %5606) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,4],f32> 
    %5608 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3259> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5609 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3260> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5610 = torch.operator "onnx.QuantizeLinear"(%5607, %5608, %5609) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],si8> 
    %5611 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3261> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5612 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3262> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5613 = torch.operator "onnx.DequantizeLinear"(%5610, %5611, %5612) : (!torch.vtensor<[?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],f32> 
    %5614 = torch.operator "onnx.Gather"(%5597, %5606) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],f32> 
    %5615 = torch.operator "onnx.Gather"(%5598, %5606) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],si64>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?],si64> 
    %5616 = torch.operator "onnx.Shape"(%5613) : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[2],si64> 
    %5617 = torch.operator "onnx.ReduceProd"(%5616) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[],si64> 
    %5618 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3263> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5619 = torch.operator "onnx.Equal"(%5617, %5618) : (!torch.vtensor<[],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[],i1> 
    %5620 = torch.operator "onnx.Cast"(%5619) {torch.onnx.to = 9 : si64} : (!torch.vtensor<[],i1>) -> !torch.vtensor<[],i1> 
    %5621 = torch.operator "onnx.If"(%5620) : (!torch.vtensor<[],i1>) -> !torch.vtensor<[?],si64> {
      %7627 = torch.operator "onnx.ReduceMax"(%5613) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[?,4],f32>) -> !torch.vtensor<[],f32> 
      %7628 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3264> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7629 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3265> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7630 = torch.operator "onnx.QuantizeLinear"(%7627, %7628, %7629) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
      %7631 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3266> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7632 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3267> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7633 = torch.operator "onnx.DequantizeLinear"(%7630, %7631, %7632) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
      %7634 = torch.operator "onnx.Cast"(%5615) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?],si64>) -> !torch.vtensor<[?],f32> 
      %7635 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3268> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7636 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3269> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7637 = torch.operator "onnx.QuantizeLinear"(%7634, %7635, %7636) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
      %7638 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3270> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7639 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3271> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7640 = torch.operator "onnx.DequantizeLinear"(%7637, %7638, %7639) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
      %7641 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3272> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7642 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3273> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7643 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3274> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7644 = torch.operator "onnx.QuantizeLinear"(%7641, %7642, %7643) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
      %7645 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3275> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7646 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3276> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7647 = torch.operator "onnx.DequantizeLinear"(%7644, %7645, %7646) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
      %7648 = torch.operator "onnx.Add"(%7633, %7647) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[],f32> 
      %7649 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3277> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7650 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3278> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7651 = torch.operator "onnx.QuantizeLinear"(%7648, %7649, %7650) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
      %7652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3279> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7653 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3280> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7654 = torch.operator "onnx.DequantizeLinear"(%7651, %7652, %7653) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
      %7655 = torch.operator "onnx.Mul"(%7640, %7654) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
      %7656 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3281> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7657 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3282> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7658 = torch.operator "onnx.QuantizeLinear"(%7655, %7656, %7657) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
      %7659 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3283> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7660 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3284> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7661 = torch.operator "onnx.DequantizeLinear"(%7658, %7659, %7660) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
      %7662 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3285> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7663 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3286> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7664 = torch.operator "onnx.QuantizeLinear"(%7661, %7662, %7663) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
      %7665 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3287> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3288> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7667 = torch.operator "onnx.DequantizeLinear"(%7664, %7665, %7666) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
      %7668 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3289> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7669 = torch.operator "onnx.Unsqueeze"(%7667, %7668) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
      %7670 = torch.operator "onnx.Add"(%5613, %7669) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,4],f32> 
      %7671 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3290> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7672 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3291> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7673 = torch.operator "onnx.QuantizeLinear"(%7670, %7671, %7672) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],si8> 
      %7674 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3292> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
      %7675 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3293> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
      %7676 = torch.operator "onnx.DequantizeLinear"(%7673, %7674, %7675) : (!torch.vtensor<[?,4],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,4],f32> 
      %7677 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3294> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7678 = torch.operator "onnx.Unsqueeze"(%7676, %7677) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,?,4],f32> 
      %7679 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3295> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7680 = torch.operator "onnx.Unsqueeze"(%5614, %7679) : (!torch.vtensor<[?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,?],f32> 
      %7681 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3296> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7682 = torch.operator "onnx.Unsqueeze"(%7680, %7681) : (!torch.vtensor<[1,?],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1,1,?],f32> 
      %7683 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3297> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7684 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3298> : tensor<1xf32>} : () -> !torch.vtensor<[1],f32> 
      %7685 = torch.operator "onnx.NonMaxSuppression"(%7678, %7682, %7683, %7684) : (!torch.vtensor<[1,?,4],f32>, !torch.vtensor<[1,1,?],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],f32>) -> !torch.vtensor<[?,3],si64> 
      %7686 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3299> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7687 = torch.operator "onnx.Gather"(%7685, %7686) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,3],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
      %7688 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3300> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7689 = torch.operator "onnx.Squeeze"(%7687, %7688) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
      torch.operator_terminator %7689 : !torch.vtensor<[?],si64>
    }, {
      %7627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3301> : tensor<0xsi64>} : () -> !torch.vtensor<[0],si64> 
      torch.operator_terminator %7627 : !torch.vtensor<[0],si64>
    }
    %5622 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3302> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5623 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3303> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5624 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3304> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5625 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3305> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5626 = torch.operator "onnx.Slice"(%5621, %5623, %5624, %5622, %5625) : (!torch.vtensor<[?],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5627 = torch.operator "onnx.Gather"(%5613, %5626) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,4],f32> 
    %5628 = torch.operator "onnx.Concat"(%5509, %5627) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[?,4],f32>) -> !torch.vtensor<[?,4],f32> 
    %5629 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3306> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5630 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3307> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5631 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3308> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5632 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3309> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5633 = torch.operator "onnx.Slice"(%5509, %5630, %5631, %5629, %5632) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5634 = torch.operator "onnx.Shape"(%5633) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[2],si64> 
    %5635 = torch.operator "onnx.ConstantOfShape"(%5634) {torch.onnx.value = dense_resource<__3310> : tensor<1xf32>} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[?,1],f32> 
    %5636 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3311> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5637 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3312> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5638 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3313> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5639 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3314> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5640 = torch.operator "onnx.Slice"(%5627, %5637, %5638, %5636, %5639) : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5641 = torch.operator "onnx.Shape"(%5640) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[2],si64> 
    %5642 = torch.operator "onnx.ConstantOfShape"(%5641) {torch.onnx.value = dense_resource<__3315> : tensor<1xf32>} : (!torch.vtensor<[2],si64>) -> !torch.vtensor<[?,1],f32> 
    %5643 = torch.operator "onnx.Concat"(%5635, %5642) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,1],f32>, !torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,1],f32> 
    %5644 = torch.operator "onnx.Concat"(%5643, %5628) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],f32>, !torch.vtensor<[?,4],f32>) -> !torch.vtensor<[?,5],f32> 
    %5645 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3316> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5646 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3317> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5647 = torch.operator "onnx.QuantizeLinear"(%5644, %5645, %5646) : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,5],si8> 
    %5648 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3318> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5649 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3319> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5650 = torch.operator "onnx.DequantizeLinear"(%5647, %5648, %5649) : (!torch.vtensor<[?,5],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?,5],f32> 
    %5651 = torch.operator "onnx.Gather"(%5509, %283) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5652 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3320> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5653 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3321> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5654 = torch.operator "onnx.QuantizeLinear"(%5651, %5652, %5653) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5655 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3322> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5656 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3323> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5657 = torch.operator "onnx.DequantizeLinear"(%5654, %5655, %5656) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5658 = torch.operator "onnx.Gather"(%5509, %284) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5659 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3324> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5660 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3325> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5661 = torch.operator "onnx.QuantizeLinear"(%5658, %5659, %5660) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5662 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3326> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5663 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3327> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5664 = torch.operator "onnx.DequantizeLinear"(%5661, %5662, %5663) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5665 = torch.operator "onnx.Sub"(%5657, %5664) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5666 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3328> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5667 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3329> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5668 = torch.operator "onnx.QuantizeLinear"(%5665, %5666, %5667) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5669 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3330> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5670 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3331> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5671 = torch.operator "onnx.DequantizeLinear"(%5668, %5669, %5670) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5672 = torch.operator "onnx.Gather"(%5509, %278) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5673 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3332> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5674 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3333> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5675 = torch.operator "onnx.QuantizeLinear"(%5672, %5673, %5674) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5676 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3334> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5677 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3335> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5678 = torch.operator "onnx.DequantizeLinear"(%5675, %5676, %5677) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5679 = torch.operator "onnx.Gather"(%5509, %282) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5680 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3336> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5681 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3337> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5682 = torch.operator "onnx.QuantizeLinear"(%5679, %5680, %5681) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5683 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3338> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5684 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3339> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5685 = torch.operator "onnx.DequantizeLinear"(%5682, %5683, %5684) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5686 = torch.operator "onnx.Sub"(%5678, %5685) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5687 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3340> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5688 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3341> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5689 = torch.operator "onnx.QuantizeLinear"(%5686, %5687, %5688) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5690 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3342> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5691 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3343> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5692 = torch.operator "onnx.DequantizeLinear"(%5689, %5690, %5691) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5693 = torch.operator "onnx.Mul"(%5671, %5692) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5694 = torch.operator "onnx.Gather"(%5627, %283) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5695 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3344> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5696 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3345> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5697 = torch.operator "onnx.QuantizeLinear"(%5694, %5695, %5696) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5698 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3346> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5699 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3347> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5700 = torch.operator "onnx.DequantizeLinear"(%5697, %5698, %5699) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5701 = torch.operator "onnx.Gather"(%5627, %284) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5702 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3348> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5703 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3349> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5704 = torch.operator "onnx.QuantizeLinear"(%5701, %5702, %5703) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5705 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3350> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5706 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3351> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5707 = torch.operator "onnx.DequantizeLinear"(%5704, %5705, %5706) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5708 = torch.operator "onnx.Sub"(%5700, %5707) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5709 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3352> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5710 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3353> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5711 = torch.operator "onnx.QuantizeLinear"(%5708, %5709, %5710) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5712 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3354> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5713 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3355> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5714 = torch.operator "onnx.DequantizeLinear"(%5711, %5712, %5713) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5715 = torch.operator "onnx.Gather"(%5627, %278) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5716 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3356> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5717 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3357> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5718 = torch.operator "onnx.QuantizeLinear"(%5715, %5716, %5717) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5719 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3358> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5720 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3359> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5721 = torch.operator "onnx.DequantizeLinear"(%5718, %5719, %5720) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5722 = torch.operator "onnx.Gather"(%5627, %282) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],f32> 
    %5723 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3360> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5724 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3361> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5725 = torch.operator "onnx.QuantizeLinear"(%5722, %5723, %5724) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5726 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3362> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5727 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3363> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5728 = torch.operator "onnx.DequantizeLinear"(%5725, %5726, %5727) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5729 = torch.operator "onnx.Sub"(%5721, %5728) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5730 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3364> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5731 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3365> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5732 = torch.operator "onnx.QuantizeLinear"(%5729, %5730, %5731) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5733 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3366> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5734 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3367> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5735 = torch.operator "onnx.DequantizeLinear"(%5732, %5733, %5734) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5736 = torch.operator "onnx.Mul"(%5714, %5735) : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5737 = torch.operator "onnx.Concat"(%5693, %5736) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?],f32>, !torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5738 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3368> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5739 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3369> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5740 = torch.operator "onnx.QuantizeLinear"(%5737, %5738, %5739) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5741 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3370> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5742 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3371> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5743 = torch.operator "onnx.DequantizeLinear"(%5740, %5741, %5742) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5744 = torch.operator "onnx.Sqrt"(%5743) : (!torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5745 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3372> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5746 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3373> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5747 = torch.operator "onnx.QuantizeLinear"(%5744, %5745, %5746) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5748 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3374> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5749 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3375> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5750 = torch.operator "onnx.DequantizeLinear"(%5747, %5748, %5749) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5751 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3376> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5752 = torch.operator "onnx.Div"(%5750, %5751) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
    %5753 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3377> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5754 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3378> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5755 = torch.operator "onnx.QuantizeLinear"(%5752, %5753, %5754) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],si8> 
    %5756 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3379> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5757 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3380> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5758 = torch.operator "onnx.DequantizeLinear"(%5755, %5756, %5757) : (!torch.vtensor<[?],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[?],f32> 
    %5759 = torch.operator "onnx.Log"(%5758) : (!torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5760 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3381> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5761 = torch.operator "onnx.Div"(%5759, %5760) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
    %5762 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3382> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5763 = torch.operator "onnx.Add"(%5761, %5762) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
    %5764 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3383> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5765 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3384> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5766 = torch.operator "onnx.QuantizeLinear"(%2, %5764, %5765) : (!torch.vtensor<[],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],si8> 
    %5767 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3385> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5768 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3386> : tensor<si8>} : () -> !torch.vtensor<[],si8> 
    %5769 = torch.operator "onnx.DequantizeLinear"(%5766, %5767, %5768) : (!torch.vtensor<[],si8>, !torch.vtensor<[],f32>, !torch.vtensor<[],si8>) -> !torch.vtensor<[],f32> 
    %5770 = torch.operator "onnx.Add"(%5763, %5769) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
    %5771 = torch.operator "onnx.Floor"(%5770) : (!torch.vtensor<[?],f32>) -> !torch.vtensor<[?],f32> 
    %5772 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3387> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5773 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3388> : tensor<f32>} : () -> !torch.vtensor<[],f32> 
    %5774 = torch.operator "onnx.Clip"(%5771, %5772, %5773) : (!torch.vtensor<[?],f32>, !torch.vtensor<[],f32>, !torch.vtensor<[],f32>) -> !torch.vtensor<[?],f32> 
    %5775 = torch.operator "onnx.Cast"(%5774) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[?],f32>) -> !torch.vtensor<[?],si64> 
    %5776 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3389> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5777 = torch.operator "onnx.Sub"(%5775, %5776) : (!torch.vtensor<[?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],si64> 
    %5778 = torch.operator "onnx.Cast"(%5777) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[?],si64>) -> !torch.vtensor<[?],si64> 
    %5779 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3390> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5780 = torch.operator "onnx.Equal"(%5778, %5779) : (!torch.vtensor<[?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],i1> 
    %5781 = torch.operator "onnx.NonZero"(%5780) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5782 = torch.operator "onnx.Transpose"(%5781) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5783 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3391> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5784 = torch.operator "onnx.Split"(%5782, %5783) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5785 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3392> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5786 = torch.operator "onnx.Squeeze"(%5784, %5785) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5787 = torch.operator "onnx.Gather"(%5650, %5786) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,5],f32> 
    %5788 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3393> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5789 = torch.operator "onnx.Gather"(%5787, %5788) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5790 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3394> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5791 = torch.operator "onnx.Shape"(%5789) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[2],si64> 
    %5792 = torch.operator "onnx.Gather"(%5791, %5790) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5793 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3395> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5794 = torch.operator "onnx.Equal"(%5792, %5793) : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],i1> 
    %5795 = torch.operator "onnx.If"(%5794) : (!torch.vtensor<[1],i1>) -> !torch.vtensor<[],f32> {
      %7627 = torch.operator "onnx.Identity"(%5789) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,1],f32> 
      torch.operator_terminator %7627 : !torch.vtensor<[?,1],f32>
    }, {
      %7627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3396> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7628 = torch.operator "onnx.Squeeze"(%5789, %7627) : (!torch.vtensor<[?,1],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],f32> 
      torch.operator_terminator %7628 : !torch.vtensor<[?],f32>
    }
    %5796 = torch.operator "onnx.Cast"(%5795) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %5797 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3397> : tensor<4xsi64>} : () -> !torch.vtensor<[4],si64> 
    %5798 = torch.operator "onnx.Gather"(%5787, %5797) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,4],f32> 
    %5799 = torch.operator "onnx.RoiAlign"(%3802, %5798, %5796) {torch.onnx.output_height = 7 : si64, torch.onnx.output_width = 7 : si64, torch.onnx.sampling_ratio = 2 : si64, torch.onnx.spatial_scale = 2.500000e-01 : f32} : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,256,7,7],f32> 
    %5800 = torch.operator "onnx.Cast"(%5799) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,256,7,7],f32>) -> !torch.vtensor<[?,256,7,7],f32> 
    %5801 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3398> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5802 = torch.operator "onnx.Equal"(%5778, %5801) : (!torch.vtensor<[?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],i1> 
    %5803 = torch.operator "onnx.NonZero"(%5802) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5804 = torch.operator "onnx.Transpose"(%5803) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5805 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3399> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5806 = torch.operator "onnx.Split"(%5804, %5805) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5807 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3400> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5808 = torch.operator "onnx.Squeeze"(%5806, %5807) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5809 = torch.operator "onnx.Gather"(%5650, %5808) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,5],f32> 
    %5810 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3401> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5811 = torch.operator "onnx.Gather"(%5809, %5810) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5812 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3402> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5813 = torch.operator "onnx.Shape"(%5811) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[2],si64> 
    %5814 = torch.operator "onnx.Gather"(%5813, %5812) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5815 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3403> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5816 = torch.operator "onnx.Equal"(%5814, %5815) : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],i1> 
    %5817 = torch.operator "onnx.If"(%5816) : (!torch.vtensor<[1],i1>) -> !torch.vtensor<[],f32> {
      %7627 = torch.operator "onnx.Identity"(%5811) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,1],f32> 
      torch.operator_terminator %7627 : !torch.vtensor<[?,1],f32>
    }, {
      %7627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3404> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7628 = torch.operator "onnx.Squeeze"(%5811, %7627) : (!torch.vtensor<[?,1],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],f32> 
      torch.operator_terminator %7628 : !torch.vtensor<[?],f32>
    }
    %5818 = torch.operator "onnx.Cast"(%5817) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %5819 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3405> : tensor<4xsi64>} : () -> !torch.vtensor<[4],si64> 
    %5820 = torch.operator "onnx.Gather"(%5809, %5819) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,4],f32> 
    %5821 = torch.operator "onnx.RoiAlign"(%3756, %5820, %5818) {torch.onnx.output_height = 7 : si64, torch.onnx.output_width = 7 : si64, torch.onnx.sampling_ratio = 2 : si64, torch.onnx.spatial_scale = 1.250000e-01 : f32} : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,256,7,7],f32> 
    %5822 = torch.operator "onnx.Cast"(%5821) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,256,7,7],f32>) -> !torch.vtensor<[?,256,7,7],f32> 
    %5823 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3406> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5824 = torch.operator "onnx.Equal"(%5778, %5823) : (!torch.vtensor<[?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],i1> 
    %5825 = torch.operator "onnx.NonZero"(%5824) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5826 = torch.operator "onnx.Transpose"(%5825) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5827 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3407> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5828 = torch.operator "onnx.Split"(%5826, %5827) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5829 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3408> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5830 = torch.operator "onnx.Squeeze"(%5828, %5829) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5831 = torch.operator "onnx.Gather"(%5650, %5830) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,5],f32> 
    %5832 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3409> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5833 = torch.operator "onnx.Gather"(%5831, %5832) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],f32> 
    %5834 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3410> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5835 = torch.operator "onnx.Shape"(%5833) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[2],si64> 
    %5836 = torch.operator "onnx.Gather"(%5835, %5834) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],si64> 
    %5837 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3411> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5838 = torch.operator "onnx.Equal"(%5836, %5837) : (!torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[1],i1> 
    %5839 = torch.operator "onnx.If"(%5838) : (!torch.vtensor<[1],i1>) -> !torch.vtensor<[],f32> {
      %7627 = torch.operator "onnx.Identity"(%5833) : (!torch.vtensor<[?,1],f32>) -> !torch.vtensor<[?,1],f32> 
      torch.operator_terminator %7627 : !torch.vtensor<[?,1],f32>
    }, {
      %7627 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3412> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
      %7628 = torch.operator "onnx.Squeeze"(%5833, %7627) : (!torch.vtensor<[?,1],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],f32> 
      torch.operator_terminator %7628 : !torch.vtensor<[?],f32>
    }
    %5840 = torch.operator "onnx.Cast"(%5839) {torch.onnx.to = 7 : si64} : (!torch.vtensor<[],f32>) -> !torch.vtensor<[],si64> 
    %5841 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3413> : tensor<4xsi64>} : () -> !torch.vtensor<[4],si64> 
    %5842 = torch.operator "onnx.Gather"(%5831, %5841) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,4],f32> 
    %5843 = torch.operator "onnx.RoiAlign"(%3710, %5842, %5840) {torch.onnx.output_height = 7 : si64, torch.onnx.output_width = 7 : si64, torch.onnx.sampling_ratio = 2 : si64, torch.onnx.spatial_scale = 6.250000e-02 : f32} : (!torch.vtensor<[2,256,?,?],f32>, !torch.vtensor<[?,4],f32>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?,256,7,7],f32> 
    %5844 = torch.operator "onnx.Cast"(%5843) {torch.onnx.to = 1 : si64} : (!torch.vtensor<[?,256,7,7],f32>) -> !torch.vtensor<[?,256,7,7],f32> 
    %5845 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3414> : tensor<si64>} : () -> !torch.vtensor<[],si64> 
    %5846 = torch.operator "onnx.Equal"(%5778, %5845) : (!torch.vtensor<[?],si64>, !torch.vtensor<[],si64>) -> !torch.vtensor<[?],i1> 
    %5847 = torch.operator "onnx.NonZero"(%5846) : (!torch.vtensor<[?],i1>) -> !torch.vtensor<[1,?],si64> 
    %5848 = torch.operator "onnx.Transpose"(%5847) {torch.onnx.perm = [1 : si64, 0 : si64]} : (!torch.vtensor<[1,?],si64>) -> !torch.vtensor<[?,1],si64> 
    %5849 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3415> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5850 = torch.operator "onnx.Split"(%5848, %5849) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?,1],si64> 
    %5851 = torch.operator "onnx.Constant"() {torch.onnx.value = dense_resource<__3416> : tensor<1xsi64>} : () -> !torch.vtensor<[1],si64> 
    %5852 = torch.operator "onnx.Squeeze"(%5850, %5851) : (!torch.vtensor<[?,1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[?],si64> 
    %5853 = torch.operator "onnx.Gather"(%5650, %5852) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[?,5],f32>, !torch.vtensor<[?],si64>) ->