docstr: transforming outliers should put the expected amount of data in each bucket
code: def test_transform_outlier(self):
      """transforming outliers should put the expected amount of data in each bucket"""
  
      arr = np.array(range(100001))
      np.random.shuffle(arr)
      d = Discretiser(method="outlier", outlier_percentile=0.2)
      d.fit(arr)
      unique, counts = np.unique(d.transform(arr), return_counts=True)
      # check all 3 buckets are used
      assert np.array_equal([0, 1, 2], unique)
      # check largest difference in outliers is 1
      print(counts)
      assert np.abs(counts[0] - counts[2]) <= 1
  
imports: import math
  import numpy
  import pytest
  from causalnex.discretiser import Discretiser
label_desc: Use image node_id 5 for calling the TestOutlier obj's underlying member method code with example usage: obj.test_transform_outlier() without return types