code: def test_leave_one_out_fit_callTwiceOnDifferentData_ExpectRefit(self):
      x_a = pd.DataFrame(
          data=["1", "2", "2", "2", "2", "2"], columns=["col_a"]
      )
      x_b = pd.DataFrame(
          data=["1", "1", "1", "2", "2", "2"], columns=["col_b"]
      )  # different values and name
      y_dummy = [True, False, True, False, True, False]
      encoder = encoders.LeaveOneOutEncoder()
      encoder.fit(x_a, y_dummy)
      encoder.fit(x_b, y_dummy)
      mapping = encoder.mapping
      self.assertEqual(1, len(mapping))
      self.assertIn(
          "col_b", mapping
      )  # the model should have the updated mapping
      expected = pd.DataFrame(
          {"sum": [2.0, 1.0], "count": [3, 3]},
          index=["1", "2"],
          columns=["sum", "count"],
      )
      np.testing.assert_equal(expected.values, mapping["col_b"].values)
  
file_path: category_encoders/tests/test_leave_one_out.py
imports: import pandas
  from unittest import TestCase
  import tests.helpers
  import numpy
  import category_encoders
label_desc: Use image node_id 3 for calling the TestLeaveOneOutEncoder obj's underlying member method code with example usage: obj.test_leave_one_out_fit_callTwiceOnDifferentData_ExpectRefit() without return types