code: def test_weighted_var():
      es = EventSegment(2)
  
      D = np.zeros((8, 4))
      for t in range(4):
          D[t, :] = (1 / np.sqrt(4 / 3)) * np.array([-1, -1, 1, 1])
      for t in range(4, 8):
          D[t, :] = (1 / np.sqrt(4 / 3)) * np.array([1, 1, -1, -1])
      mean_pat = D[[0, 4], :].T
  
      weights = np.zeros((8, 2))
      weights[:, 0] = [1, 1, 1, 1, 0, 0, 0, 0]
      weights[:, 1] = [0, 0, 0, 0, 1, 1, 1, 1]
      assert np.array_equal(
          es.calc_weighted_event_var(D, weights, mean_pat), [0, 0]
      ), "Failed to compute variance with 0/1 weights"
  
      weights[:, 0] = [1, 1, 1, 1, 0.5, 0.5, 0.5, 0.5]
      weights[:, 1] = [0.5, 0.5, 0.5, 0.5, 1, 1, 1, 1]
      true_var = (4 * 0.5 * 12) / (6 - 5 / 6) * np.ones(2) / 4
      assert np.allclose(
          es.calc_weighted_event_var(D, weights, mean_pat), true_var
      ), "Failed to compute variance with fractional weights"
  
file_path: brainiak/tests/eventseg/test_event.py
imports: from brainiak.eventseg.event import EventSegment
  from scipy.special import comb
  import numpy
  import pytest
  from sklearn.exceptions import NotFittedError
label_desc: Use image node_id 5 for calling a global function with example usage: test_weighted_var() without return types