test/expect/TestJit.test_alexnet.expect

graph(%0 : Double(1, 3, 224, 224)
      %1 : Double(64, 3, 11, 11)
      %2 : Double(64)
      %3 : Double(192, 64, 5, 5)
      %4 : Double(192)
      %5 : Double(384, 192, 3, 3)
      %6 : Double(384)
      %7 : Double(256, 384, 3, 3)
      %8 : Double(256)
      %9 : Double(256, 256, 3, 3)
      %10 : Double(256)
      %11 : Double(4096, 9216)
      %12 : Double(4096)
      %13 : Double(4096, 4096)
      %14 : Double(4096)
      %15 : Double(1000, 4096)
      %16 : Double(1000)) {
  %17 : int = prim::Constant[value=4](), scope: AlexNet/Sequential[features]/Conv2d[0]
  %18 : int[] = prim::ListConstruct(%17, %17), scope: AlexNet/Sequential[features]/Conv2d[0]
  %19 : int = prim::Constant[value=2](), scope: AlexNet/Sequential[features]/Conv2d[0]
  %20 : int[] = prim::ListConstruct(%19, %19), scope: AlexNet/Sequential[features]/Conv2d[0]
  %21 : int = prim::Constant[value=1](), scope: AlexNet/Sequential[features]/Conv2d[0]
  %22 : int[] = prim::ListConstruct(%21, %21), scope: AlexNet/Sequential[features]/Conv2d[0]
  %23 : int = prim::Constant[value=0](), scope: AlexNet/Sequential[features]/Conv2d[0]
  %24 : int[] = prim::ListConstruct(%23, %23), scope: AlexNet/Sequential[features]/Conv2d[0]
  %25 : Double(1, 64, 55, 55) = aten::_convolution(%0, %1, %2, %18, %20, %22, %23, %24, %21, %23, %23, %21), scope: AlexNet/Sequential[features]/Conv2d[0]
  %26 : Double(1, 64, 55, 55) = aten::threshold(%25, %23, %23), scope: AlexNet/Sequential[features]/ReLU[1]
  %27 : int = prim::Constant[value=3](), scope: AlexNet/Sequential[features]/MaxPool2d[2]
  %28 : int[] = prim::ListConstruct(%27, %27), scope: AlexNet/Sequential[features]/MaxPool2d[2]
  %29 : Double(1, 64, 27, 27), %30 : Long(1, 64, 27, 27) = aten::max_pool2d_with_indices(%26, %28, %20, %24, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[2]
  %31 : Double(1, 192, 27, 27) = aten::_convolution(%29, %3, %4, %22, %20, %22, %23, %24, %21, %23, %23, %21), scope: AlexNet/Sequential[features]/Conv2d[3]
  %32 : Double(1, 192, 27, 27) = aten::threshold(%31, %23, %23), scope: AlexNet/Sequential[features]/ReLU[4]
  %33 : Double(1, 192, 13, 13), %34 : Long(1, 192, 13, 13) = aten::max_pool2d_with_indices(%32, %28, %20, %24, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[5]
  %35 : Double(1, 384, 13, 13) = aten::_convolution(%33, %5, %6, %22, %22, %22, %23, %24, %21, %23, %23, %21), scope: AlexNet/Sequential[features]/Conv2d[6]
  %36 : Double(1, 384, 13, 13) = aten::threshold(%35, %23, %23), scope: AlexNet/Sequential[features]/ReLU[7]
  %37 : Double(1, 256, 13, 13) = aten::_convolution(%36, %7, %8, %22, %22, %22, %23, %24, %21, %23, %23, %21), scope: AlexNet/Sequential[features]/Conv2d[8]
  %38 : Double(1, 256, 13, 13) = aten::threshold(%37, %23, %23), scope: AlexNet/Sequential[features]/ReLU[9]
  %39 : Double(1, 256, 13, 13) = aten::_convolution(%38, %9, %10, %22, %22, %22, %23, %24, %21, %23, %23, %21), scope: AlexNet/Sequential[features]/Conv2d[10]
  %40 : Double(1, 256, 13, 13) = aten::threshold(%39, %23, %23), scope: AlexNet/Sequential[features]/ReLU[11]
  %41 : Double(1, 256, 6, 6), %42 : Long(1, 256, 6, 6) = aten::max_pool2d_with_indices(%40, %28, %20, %24, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[12]
  %43 : int = aten::size(%41, %23), scope: AlexNet
  %44 : Long() = prim::NumToTensor(%43), scope: AlexNet
  %45 : int = prim::TensorToNum(%44), scope: AlexNet
  %46 : int = prim::Constant[value=9216](), scope: AlexNet
  %47 : int[] = prim::ListConstruct(%45, %46), scope: AlexNet
  %48 : Double(1, 9216) = aten::view(%41, %47), scope: AlexNet
  %49 : float = prim::Constant[value=0.5](), scope: AlexNet/Sequential[classifier]/Dropout[0]
  %50 : Double(1!, 9216) = aten::dropout(%48, %49, %21), scope: AlexNet/Sequential[classifier]/Dropout[0]
  %51 : Double(9216!, 4096!) = aten::t(%11), scope: AlexNet/Sequential[classifier]/Linear[1]
  %52 : int = prim::Constant[value=4096](), scope: AlexNet/Sequential[classifier]/Linear[1]
  %53 : int[] = prim::ListConstruct(%21, %52), scope: AlexNet/Sequential[classifier]/Linear[1]
  %54 : Double(1, 4096) = aten::expand(%12, %53, %21), scope: AlexNet/Sequential[classifier]/Linear[1]
  %55 : Double(1, 4096) = aten::addmm(%54, %50, %51, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[1]
  %56 : Double(1, 4096) = aten::threshold(%55, %23, %23), scope: AlexNet/Sequential[classifier]/ReLU[2]
  %57 : Double(1!, 4096) = aten::dropout(%56, %49, %21), scope: AlexNet/Sequential[classifier]/Dropout[3]
  %58 : Double(4096!, 4096!) = aten::t(%13), scope: AlexNet/Sequential[classifier]/Linear[4]
  %59 : Double(1, 4096) = aten::expand(%14, %53, %21), scope: AlexNet/Sequential[classifier]/Linear[4]
  %60 : Double(1, 4096) = aten::addmm(%59, %57, %58, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[4]
  %61 : Double(1, 4096) = aten::threshold(%60, %23, %23), scope: AlexNet/Sequential[classifier]/ReLU[5]
  %62 : Double(4096!, 1000!) = aten::t(%15), scope: AlexNet/Sequential[classifier]/Linear[6]
  %63 : int = prim::Constant[value=1000](), scope: AlexNet/Sequential[classifier]/Linear[6]
  %64 : int[] = prim::ListConstruct(%21, %63), scope: AlexNet/Sequential[classifier]/Linear[6]
  %65 : Double(1, 1000) = aten::expand(%16, %64, %21), scope: AlexNet/Sequential[classifier]/Linear[6]
  %66 : Double(1, 1000) = aten::addmm(%65, %61, %62, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[6]
  return (%66);
}