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TestJit.test_alexnet.expect
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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 : bool = prim::Constant[value=0](), scope: AlexNet/Sequential[features]/Conv2d[0]
%24 : int = prim::Constant[value=0](), scope: AlexNet/Sequential[features]/Conv2d[0]
%25 : int[] = prim::ListConstruct(%24, %24), scope: AlexNet/Sequential[features]/Conv2d[0]
%26 : bool = prim::Constant[value=1](), scope: AlexNet/Sequential[features]/Conv2d[0]
%27 : Double(1, 64, 55, 55) = aten::_convolution(%0, %1, %2, %18, %20, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[0]
%28 : Double(1, 64, 55, 55) = aten::threshold(%27, %24, %24), scope: AlexNet/Sequential[features]/ReLU[1]
%29 : int = prim::Constant[value=3](), scope: AlexNet/Sequential[features]/MaxPool2d[2]
%30 : int[] = prim::ListConstruct(%29, %29), scope: AlexNet/Sequential[features]/MaxPool2d[2]
%31 : Double(1, 64, 27, 27), %32 : Long(1, 64, 27, 27) = aten::max_pool2d_with_indices(%28, %30, %20, %25, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[2]
%33 : Double(1, 192, 27, 27) = aten::_convolution(%31, %3, %4, %22, %20, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[3]
%34 : Double(1, 192, 27, 27) = aten::threshold(%33, %24, %24), scope: AlexNet/Sequential[features]/ReLU[4]
%35 : Double(1, 192, 13, 13), %36 : Long(1, 192, 13, 13) = aten::max_pool2d_with_indices(%34, %30, %20, %25, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[5]
%37 : Double(1, 384, 13, 13) = aten::_convolution(%35, %5, %6, %22, %22, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[6]
%38 : Double(1, 384, 13, 13) = aten::threshold(%37, %24, %24), scope: AlexNet/Sequential[features]/ReLU[7]
%39 : Double(1, 256, 13, 13) = aten::_convolution(%38, %7, %8, %22, %22, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[8]
%40 : Double(1, 256, 13, 13) = aten::threshold(%39, %24, %24), scope: AlexNet/Sequential[features]/ReLU[9]
%41 : Double(1, 256, 13, 13) = aten::_convolution(%40, %9, %10, %22, %22, %22, %23, %25, %21, %23, %23, %26), scope: AlexNet/Sequential[features]/Conv2d[10]
%42 : Double(1, 256, 13, 13) = aten::threshold(%41, %24, %24), scope: AlexNet/Sequential[features]/ReLU[11]
%43 : Double(1, 256, 6, 6), %44 : Long(1, 256, 6, 6) = aten::max_pool2d_with_indices(%42, %30, %20, %25, %22, %23), scope: AlexNet/Sequential[features]/MaxPool2d[12]
%45 : int = aten::size(%43, %24), scope: AlexNet
%46 : Long() = prim::NumToTensor(%45), scope: AlexNet
%47 : int = prim::TensorToNum(%46), scope: AlexNet
%48 : int = prim::Constant[value=9216](), scope: AlexNet
%49 : int[] = prim::ListConstruct(%47, %48), scope: AlexNet
%50 : Double(1, 9216) = aten::view(%43, %49), scope: AlexNet
%51 : float = prim::Constant[value=0.5](), scope: AlexNet/Sequential[classifier]/Dropout[0]
%52 : Double(1, 9216) = aten::dropout(%50, %51, %26), scope: AlexNet/Sequential[classifier]/Dropout[0]
%53 : Double(9216!, 4096!) = aten::t(%11), scope: AlexNet/Sequential[classifier]/Linear[1]
%54 : int = prim::Constant[value=4096](), scope: AlexNet/Sequential[classifier]/Linear[1]
%55 : int[] = prim::ListConstruct(%21, %54), scope: AlexNet/Sequential[classifier]/Linear[1]
%56 : Double(1, 4096) = aten::expand(%12, %55, %26), scope: AlexNet/Sequential[classifier]/Linear[1]
%57 : Double(1, 4096) = aten::addmm(%56, %52, %53, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[1]
%58 : Double(1, 4096) = aten::threshold(%57, %24, %24), scope: AlexNet/Sequential[classifier]/ReLU[2]
%59 : Double(1, 4096) = aten::dropout(%58, %51, %26), scope: AlexNet/Sequential[classifier]/Dropout[3]
%60 : Double(4096!, 4096!) = aten::t(%13), scope: AlexNet/Sequential[classifier]/Linear[4]
%61 : Double(1, 4096) = aten::expand(%14, %55, %26), scope: AlexNet/Sequential[classifier]/Linear[4]
%62 : Double(1, 4096) = aten::addmm(%61, %59, %60, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[4]
%63 : Double(1, 4096) = aten::threshold(%62, %24, %24), scope: AlexNet/Sequential[classifier]/ReLU[5]
%64 : Double(4096!, 1000!) = aten::t(%15), scope: AlexNet/Sequential[classifier]/Linear[6]
%65 : int = prim::Constant[value=1000](), scope: AlexNet/Sequential[classifier]/Linear[6]
%66 : int[] = prim::ListConstruct(%21, %65), scope: AlexNet/Sequential[classifier]/Linear[6]
%67 : Double(1, 1000) = aten::expand(%16, %66, %26), scope: AlexNet/Sequential[classifier]/Linear[6]
%68 : Double(1, 1000) = aten::addmm(%67, %63, %64, %21, %21), scope: AlexNet/Sequential[classifier]/Linear[6]
return (%68);
}