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)
    net_modifier = ComputeNormForBlobs(
        blobs=['fc1_w'],
        logging_frequency=10,
        compute_averaged_norm=True,
        row_index=1
    )
    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_row_1_averaged_l2_norm = workspace.FetchBlob('fc1_w_row_1_averaged_l2_norm')
    self.assertEqual(fc1_w_row_1_averaged_l2_norm.size, 1)
    self.assertAlmostEqual(fc1_w_row_1_averaged_l2_norm[0],
                           np.linalg.norm(fc1_w[1])**2 / fc1_w[1].size,
                           delta=1e-5)