docstr: Construct LinearOperatorLowRankUpdate covariance with constant params.
docstr_tok: ["Construct","LinearOperatorLowRankUpdate","covariance","with","constant","params","."]
code: def _construct_cov_operator(
      self,
      diag_shape,
      update_shape,
      include_diag_update=None,
      dtype=np.float32,
  ):
      """Construct LinearOperatorLowRankUpdate covariance with constant params."""
      base_operator = tf.linalg.LinearOperatorDiag(
          np.random.uniform(low=1.0, high=2.0, size=diag_shape).astype(
              dtype
          ),
          is_positive_definite=True,
      )
      u = np.random.normal(size=update_shape).astype(dtype)
      if include_diag_update:
          diag_update = np.random.uniform(
              size=update_shape[:-2] + update_shape[-1:],
              low=1.0,
              high=2.0,
          ).astype(dtype)
      else:
          diag_update = None
      return tf.linalg.LinearOperatorLowRankUpdate(
          base_operator, u=u, diag_update=diag_update
      )
  
imports: import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import mvn_low_rank_update_linear_operator_covariance
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 2 for calling the MultivariateNormalLowRankUpdateLinearOperatorCovarianceTest obj's underlying member method code with example usage: obj._construct_cov_operator(diag_shape, update_shape, include_diag_update, dtype) and returns: tf