def METHOD_NAME(self):
    self.rawimg = numpy.random.randint(0, 256, (1, 200, 200, 1, 1))
    self.binimg = self.rawimg > 100
    cc = vigra.analysis.labelImageWithBackground(self.binimg.squeeze().astype(numpy.uint8))
    nobj = numpy.max(cc)
    if nobj < 7:
        # somehow, we randomly didn't get enough objects.
        # generate some bright points instead
        self.rawimg[3, 3] = 255
        self.rawimg[6, 6] = 255
        self.rawimg[10, 10] = 255
        self.rawimg[20, 20] = 255
        self.rawimg[30, 30] = 255
        self.rawimg[50, 50] = 255
        self.rawimg[100, 100] = 255
        self.rawimg[150, 150] = 255
        self.binimg = self.rawimg > 100
        cc = vigra.analysis.labelImageWithBackground(self.binimg.squeeze())
    self.segmimg = cc.reshape((1,) + cc.shape + (1,) + (1,))
    self.rawimg = vigra.taggedView(self.rawimg, "txyzc")
    self.binimg = vigra.taggedView(self.binimg, "txyzc")
    self.segmimg = vigra.taggedView(self.segmimg, "txyzc")
    self.features = {"Bad Plugin": {"bad_feature_1": {}, "bad_feature_2": {}}}
    featureArray1 = numpy.zeros((nobj + 1, 1))
    featureArray1[2][0] = numpy.Inf
    featureArray1[4][0] = numpy.inf
    featureArray2 = numpy.zeros((nobj + 1, 1))
    featureArray2[1][0] = numpy.NaN
    featureArray2[3][0] = numpy.nan
    self.featureArrays = {0: {"Bad Plugin": {"bad_feature_1": featureArray1, "bad_feature_2": featureArray2}}}
    g = Graph()
    self.op = OpObjectClassification(graph=g)
    self.op.RawImages.setValues([self.rawimg])
    self.op.BinaryImages.setValues([self.binimg])
    self.op.SegmentationImages.setValues([self.segmimg])
    self.op.ObjectFeatures.setValues([self.featureArrays])
    self.op.ComputedFeatureNames.setValue(self.features)
    self.op.SelectedFeatures.setValue(self.features)