code: def test_sum(self):
      dataset = Dataset("adult")
      model = self.fit("Logloss", dataset)
      model1 = self.normalize_model(model, dataset, "test_file")
      model2 = self.normalize_model(model, dataset, "train_file")
      s1, b1 = self.get_scale_bias(model1)
      s2, b2 = self.get_scale_bias(model2)
  
      # Pick weights w1, w2 so that model_sum = w1 * model1 + w2 * model2
      det = s1 * b2 - s2 * b1
      w1 = b2 / det if det else 0.5
      w2 = -b1 / det if det else 0.5
  
      model_sum = get_test_output_path("model_sum{}.bin")
      yc.execute(
          [
              CATBOOST_PATH,
              "model-sum",
              "--model-with-weight",
              "{}={}".format(model1, w1),
              "--model-with-weight",
              "{}={}".format(model2, w2),
              "--output-path",
              model_sum,
          ]
      )
      eval_orig = self.eval_model(model, dataset, "test_file")
      eval_sum = self.eval_model(model_sum, dataset, "test_file")
      yc.execute(
          get_limited_precision_dsv_diff_tool(1e-8)
          + [eval_orig, eval_sum]
      )
  
imports: import os
  import pytest
  import re
  import yatest.common
  from lib import data_file, get_limited_precision_dsv_diff_tool
label_desc: Use image node_id 10 for calling the TestModeNormalizeModel obj's underlying member method code with example usage: obj.test_sum() without return types