docstr: Fitting gauss data should transform to predictable buckets
code: def test_transform_gauss(self):
      """Fitting gauss data should transform to predictable buckets"""
  
      arr = np.random.normal(loc=0, scale=1, size=1000000)
      np.random.shuffle(arr)
      d = Discretiser(method="quantile", num_buckets=4)
      d.fit(arr)
      unique, counts = np.unique(d.transform(arr), return_counts=True)
      # check all 4 buckets are used
      assert np.array_equal([0, 1, 2, 3], unique)
      assert np.array_equal([250000 for n in range(4)], counts)
  
imports: import math
  import numpy
  import pytest
  from causalnex.discretiser import Discretiser
label_desc: Use image node_id 3 for calling the TestQuantile obj's underlying member method code with example usage: obj.test_transform_gauss() without return types