code: def test_transform_after_fit(self):
      # On both X_fit and X_transf, the categories 3, 4, 5, 6, 7 are present.
      X_fit = (
          self.X1
      )  # Here categories 3, 4, 5 have ocurrence above 10%
      X_transf = (
          self.X2
      )  # Here it is the opposite, just categs 6 and 7 are above 10%
  
      mc = MinorityCoalescer(minimum_fraction=0.1).fit(X_fit)
  
      # transform() should coalesce categories as learned during fit.
      # Category distribution in X_transf should be irrelevant.
      Y = mc.transform(X_transf)
      for col in range(Y.shape[1]):
          hist = np.histogram(Y[:, col], bins=np.arange(1, 7))
          np.testing.assert_array_almost_equal(
              hist[0], [85, 0, 5, 5, 5]
          )
  
file_path: auto-sklearn/test/test_pipeline/implementations/test_MinorityCoalescer.py
imports: import numpy
  import scipy.sparse
  from autosklearn.pipeline.implementations.MinorityCoalescer import MinorityCoalescer
  import unittest
label_desc: Use image node_id 7 for calling the MinorityCoalescerTest obj's underlying member method code with example usage: obj.test_transform_after_fit() without return types