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)
    num_buckets = 20
    lower_bound = 0.2
    upper_bound = 0.8
    accumulate = False
    net_modifier = ComputeHistogramForBlobs(blobs=['fc1_w', 'fc2_w'],
                                            logging_frequency=10,
                                            num_buckets=num_buckets,
                                            lower_bound=lower_bound,
                                            upper_bound=upper_bound,
                                            accumulate=accumulate)
    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_curr_normalized_hist = workspace.FetchBlob('fc1_w_curr_normalized_hist')
    cur_hist, acc_hist = self.histogram(fc1_w,
                                        lower_bound=lower_bound,
                                        upper_bound=upper_bound,
                                        num_buckets=num_buckets)
    self.assertEqual(fc1_w_curr_normalized_hist.size, num_buckets + 2)
    self.assertAlmostEqual(np.linalg.norm(
        fc1_w_curr_normalized_hist - cur_hist), 0.0, delta=1e-5)
    self.assertEqual(len(model.net.Proto().op), 12)
    assert model.net.output_record() is None