class_docstr: Short test of this derived class -- most tests are for base class.
docstr: Construct LinearOperatorLowRankUpdate covariance with random params.
docstr_tok: ["Construct","LinearOperatorLowRankUpdate","covariance","with","random","params","."]
code: def _construct_mvn(
      self, loc_shape, diag_shape, update_shape, dtype=np.float32
  ):
      """Construct LinearOperatorLowRankUpdate covariance with random params."""
      if loc_shape is not None:
          loc = np.random.normal(size=loc_shape).astype(dtype)
      else:
          loc = None
      cov_diag_factor = np.random.uniform(
          low=1.0, high=2.0, size=diag_shape
      ).astype(dtype)
      cov_perturb_factor = np.random.normal(size=update_shape).astype(
          dtype
      )
      return mvdplrc.MultivariateNormalDiagPlusLowRankCovariance(
          loc=loc,
          cov_diag_factor=cov_diag_factor,
          cov_perturb_factor=cov_perturb_factor,
      )
  
imports: import numpy
  from tensorflow_probability.python.distributions import mvn_diag_plus_low_rank_covariance
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 1 for calling the MultivariateNormalLowRankUpdateLinearOperatorCovarianceTest obj's underlying member method code with example usage: obj._construct_mvn(loc_shape, diag_shape, update_shape, dtype) and returns: mvdplrc