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 : Double(1, 64, 55, 55) = aten::_convolution[stride=[4, 4], padding=[2, 2], dilation=[1, 1], transposed=0, output_padding=[0, 0], groups=1, benchmark=0, deterministic=0, cudnn_enabled=1](%0, %1, %2), scope: AlexNet/Sequential[features]/Conv2d[0]
  %18 : Double(1, 64, 55, 55) = aten::threshold[threshold={0}, value={0}](%17), scope: AlexNet/Sequential[features]/ReLU[1]
  %19 : Double(1, 64, 27, 27), %20 : Long(1, 64, 27, 27) = aten::max_pool2d[kernel_size=[3, 3], stride=[2, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=0](%18), scope: AlexNet/Sequential[features]/MaxPool2d[2]
  %21 : Double(1, 192, 27, 27) = aten::_convolution[stride=[1, 1], padding=[2, 2], dilation=[1, 1], transposed=0, output_padding=[0, 0], groups=1, benchmark=0, deterministic=0, cudnn_enabled=1](%19, %3, %4), scope: AlexNet/Sequential[features]/Conv2d[3]
  %22 : Double(1, 192, 27, 27) = aten::threshold[threshold={0}, value={0}](%21), scope: AlexNet/Sequential[features]/ReLU[4]
  %23 : Double(1, 192, 13, 13), %24 : Long(1, 192, 13, 13) = aten::max_pool2d[kernel_size=[3, 3], stride=[2, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=0](%22), scope: AlexNet/Sequential[features]/MaxPool2d[5]
  %25 : Double(1, 384, 13, 13) = aten::_convolution[stride=[1, 1], padding=[1, 1], dilation=[1, 1], transposed=0, output_padding=[0, 0], groups=1, benchmark=0, deterministic=0, cudnn_enabled=1](%23, %5, %6), scope: AlexNet/Sequential[features]/Conv2d[6]
  %26 : Double(1, 384, 13, 13) = aten::threshold[threshold={0}, value={0}](%25), scope: AlexNet/Sequential[features]/ReLU[7]
  %27 : Double(1, 256, 13, 13) = aten::_convolution[stride=[1, 1], padding=[1, 1], dilation=[1, 1], transposed=0, output_padding=[0, 0], groups=1, benchmark=0, deterministic=0, cudnn_enabled=1](%26, %7, %8), scope: AlexNet/Sequential[features]/Conv2d[8]
  %28 : Double(1, 256, 13, 13) = aten::threshold[threshold={0}, value={0}](%27), scope: AlexNet/Sequential[features]/ReLU[9]
  %29 : Double(1, 256, 13, 13) = aten::_convolution[stride=[1, 1], padding=[1, 1], dilation=[1, 1], transposed=0, output_padding=[0, 0], groups=1, benchmark=0, deterministic=0, cudnn_enabled=1](%28, %9, %10), scope: AlexNet/Sequential[features]/Conv2d[10]
  %30 : Double(1, 256, 13, 13) = aten::threshold[threshold={0}, value={0}](%29), scope: AlexNet/Sequential[features]/ReLU[11]
  %31 : Double(1, 256, 6, 6), %32 : Long(1, 256, 6, 6) = aten::max_pool2d[kernel_size=[3, 3], stride=[2, 2], padding=[0, 0], dilation=[1, 1], ceil_mode=0](%30), scope: AlexNet/Sequential[features]/MaxPool2d[12]
  %33 : Long() = aten::size[dim=0](%31), scope: AlexNet
  %34 : Long() = prim::Constant[value={9216}](), scope: AlexNet
  %35 : Dynamic = aten::stack[dim=0](%33, %34), scope: AlexNet
  %36 : Double(1, 9216) = aten::view(%31, %35), scope: AlexNet
  %37 : Double(1, 9216) = ^Dropout(0.5, True, False)(%36), scope: AlexNet/Sequential[classifier]/Dropout[0]
  %38 : Double(9216!, 4096!) = aten::t(%11), scope: AlexNet/Sequential[classifier]/Linear[1]
  %39 : Double(1, 4096) = aten::expand[size=[1, 4096], implicit=1](%12), scope: AlexNet/Sequential[classifier]/Linear[1]
  %40 : Double(1, 4096) = aten::addmm[beta={1}, alpha={1}](%39, %37, %38), scope: AlexNet/Sequential[classifier]/Linear[1]
  %41 : Double(1, 4096) = aten::threshold[threshold={0}, value={0}](%40), scope: AlexNet/Sequential[classifier]/ReLU[2]
  %42 : Double(1, 4096) = ^Dropout(0.5, True, False)(%41), scope: AlexNet/Sequential[classifier]/Dropout[3]
  %43 : Double(4096!, 4096!) = aten::t(%13), scope: AlexNet/Sequential[classifier]/Linear[4]
  %44 : Double(1, 4096) = aten::expand[size=[1, 4096], implicit=1](%14), scope: AlexNet/Sequential[classifier]/Linear[4]
  %45 : Double(1, 4096) = aten::addmm[beta={1}, alpha={1}](%44, %42, %43), scope: AlexNet/Sequential[classifier]/Linear[4]
  %46 : Double(1, 4096) = aten::threshold[threshold={0}, value={0}](%45), scope: AlexNet/Sequential[classifier]/ReLU[5]
  %47 : Double(4096!, 1000!) = aten::t(%15), scope: AlexNet/Sequential[classifier]/Linear[6]
  %48 : Double(1, 1000) = aten::expand[size=[1, 1000], implicit=1](%16), scope: AlexNet/Sequential[classifier]/Linear[6]
  %49 : Double(1, 1000) = aten::addmm[beta={1}, alpha={1}](%48, %46, %47), scope: AlexNet/Sequential[classifier]/Linear[6]
  return (%49);
}