code: def test_compute_statistics_for_blobs_modify_output_record(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 = ComputeStatisticsForBlobs(
          blobs=["fc1_w", "fc2_w"],
          logging_frequency=10,
      )
  
      net_modifier(model.net, modify_output_record=True)
  
      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_summary = workspace.FetchBlob("fc1_w_summary")
  
      # std is unbiased here
      stats_ref = np.array(
          [
              fc1_w.flatten().min(),
              fc1_w.flatten().max(),
              fc1_w.flatten().mean(),
              fc1_w.flatten().std(ddof=1),
          ]
      )
  
      self.assertAlmostEqual(
          np.linalg.norm(stats_ref - fc1_w_summary), 0, delta=1e-5
      )
      self.assertEqual(fc1_w_summary.size, 4)
  
      self.assertEqual(len(model.net.Proto().op), 8)
      assert (
          "fc1_w" + net_modifier.field_name_suffix()
          in model.net.output_record().field_blobs()
      )
      assert (
          "fc2_w" + net_modifier.field_name_suffix()
          in model.net.output_record().field_blobs()
      )
  
imports: import unittest
  from caffe2.python import workspace, brew, model_helper
  from caffe2.python.modeling.compute_statistics_for_blobs import ComputeStatisticsForBlobs
  import numpy
label_desc: Use image node_id 2 for calling the ComputeStatisticsForBlobsTest obj's underlying member method code with example usage: obj.test_compute_statistics_for_blobs_modify_output_record() without return types