code: def test_Cov3FactorMaskedKron(seeded_rng):
      assert m % 4 == 0
      dim1 = int(m / 4)
      dim2 = 2
      dim3 = 2
  
      mask = np.random.binomial(1, 0.5, m).astype(np.int32)
  
      if sum(mask == 0):
          mask[0] = 1
      mask_indices = np.nonzero(mask)[0]
  
      cov = CovKroneckerFactored(sizes=[dim1, dim2, dim3], mask=mask)
  
      L1 = (cov.L[0]).numpy()
      L2 = (cov.L[1]).numpy()
      L3 = (cov.L[2]).numpy()
      cov_np_factor = np.kron(L1, np.kron(L2, L3))[
          np.ix_(mask_indices, mask_indices)
      ]
      cov_np = np.dot(cov_np_factor, cov_np_factor.transpose())
      logdet_np, sinv_np, sinvx_np = logdet_sinv_np(
          X[mask_indices, :], cov_np
      )
  
      assert_allclose(logdet_np, cov.logdet, rtol=rtol, atol=atol)
      assert_allclose(
          sinv_np,
          cov.solve(eye).numpy()[np.ix_(mask_indices, mask_indices)],
          rtol=rtol,
          atol=atol,
      )
      assert_allclose(
          sinvx_np,
          cov.solve(X_tf).numpy()[mask_indices, :],
          rtol=rtol,
          atol=atol,
      )
  
imports: import pytest
  import numpy
  from numpy.testing import assert_allclose
  from scipy.stats import norm, wishart, invgamma, invwishart
  import tensorflow
  from brainiak.matnormal.covs import CovIdentity, CovAR1, CovIsotropic, CovDiagonal, CovDiagonalGammaPrior, CovUnconstrainedCholesky, CovUnconstrainedCholeskyWishartReg, CovUnconstrainedInvCholesky, CovKroneckerFactored
label_desc: Use image node_id 14 for calling a global function with example usage: test_Cov3FactorMaskedKron(seeded_rng) without return types