def METHOD_NAME(self):
    # We randomly select a shape to test the speed. Intentionally we
    # test a batch size of 1 since this may be the most frequent use
    # case for MKL during deployment time.
    X = np.random.rand(1, 3, 224, 224).astype(np.float32) - 0.5
    W = np.random.rand(64, 3, 11, 11).astype(np.float32) - 0.5
    b = np.random.rand(64).astype(np.float32) - 0.5
    mkl_do = core.DeviceOption(caffe2_pb2.MKLDNN)
    # Makes sure that feed works.
    workspace.FeedBlob("X", X)
    workspace.FeedBlob("W", W)
    workspace.FeedBlob("b", b)
    workspace.FeedBlob("X_mkl", X, device_option=mkl_do)
    workspace.FeedBlob("W_mkl", W, device_option=mkl_do)
    workspace.FeedBlob("b_mkl", b, device_option=mkl_do)
    net = core.Net("test")
    net.Conv(["X", "W", "b"], "C", pad=1, stride=1, kernel=11)
    net.Conv(["X_mkl", "W_mkl", "b_mkl"], "C_mkl",
             pad=1, stride=1, kernel=11, device_option=mkl_do)
    net.Relu("C", "R")
    net.Relu("C_mkl", "R_mkl", device_option=mkl_do)
    net.AveragePool("R", "Y", stride=2, kernel=3)
    net.AveragePool("R_mkl", "Y_mkl",
             stride=2, kernel=3, device_option=mkl_do)
    workspace.CreateNet(net)
    workspace.RunNet(net)
    # makes sure that the results are good.
    np.testing.assert_allclose(
        workspace.FetchBlob("Y"),
        workspace.FetchBlob("Y_mkl"),
        atol=1e-2,
        rtol=1e-2)
    runtime = workspace.BenchmarkNet(net.Proto().name, 1, 100, True)