def METHOD_NAME(self):
    model = model_helper.ModelHelper(name="test")
    data = model.net.AddExternalInput("data")
    fc1 = brew.fc(model, data, "fc1", dim_in=4, dim_out=2)
    # no operator name set, will use default
    brew.fc(model, fc1, "fc2", dim_in=2, dim_out=1)
    net_modifier = ComputeNormForBlobs(
        blobs=['fc1_w', 'fc2_w'],
        logging_frequency=10,
        p=1,
        compute_averaged_norm=True,
    )
    net_modifier(model.net)
    workspace.FeedBlob('data', np.random.rand(10, 4).astype(np.float32))
    workspace.RunNetOnce(model.param_init_net)
    workspace.RunNetOnce(model.net)
    fc1_w = workspace.FetchBlob('fc1_w')
    fc1_w_averaged_l1_norm = workspace.FetchBlob('fc1_w_averaged_l1_norm')
    self.assertEqual(fc1_w_averaged_l1_norm.size, 1)
    self.assertAlmostEqual(fc1_w_averaged_l1_norm[0],
                           np.sum(np.abs(fc1_w)) / fc1_w.size,
                           delta=1e-5)
    self.assertEqual(len(model.net.Proto().op), 10)