code: def test_format_and_aggregate_local_contributions_2(self):
      self.test_backend._case = "classification"
      self.test_backend._classes = [0, 1]
      self.test_backend.preprocessing = None
  
      x = pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["a", "b", "c"])
  
      contributions = [
          pd.DataFrame(
              [[-0.1, 0.2, -0.3], [0.1, -0.2, 0.3]],
              columns=["a", "b", "c"],
          ),
          pd.DataFrame(
              [[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]],
              columns=["a", "b", "c"],
          ),
      ]
      res_contributions = (
          self.test_backend.format_and_aggregate_local_contributions(
              x, contributions
          )
      )
  
      assert len(res_contributions) == 2
      assert_frame_equal(
          res_contributions[0],
          pd.DataFrame(
              [[-0.1, 0.2, -0.3], [0.1, -0.2, 0.3]],
              columns=["a", "b", "c"],
          ),
      )
      assert_frame_equal(
          res_contributions[1],
          pd.DataFrame(
              [[0.1, 0.2, 0.3], [0.1, 0.2, 0.3]],
              columns=["a", "b", "c"],
          ),
      )
  
imports: import unittest
  from unittest.mock import patch
  import types
  import numpy
  import pandas
  import category_encoders
  from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
  from pandas.testing import assert_frame_equal, assert_series_equal
  from shapash.backend.base_backend import BaseBackend, _needs_preprocessing
  from shapash.explainer.smart_state import SmartState
label_desc: Use image node_id 9 for calling the TestBaseBackend obj's underlying member method code with example usage: obj.test_format_and_aggregate_local_contributions_2() without return types