code: def _zero_dimensional_mvndiag(dtype):
      """Build a zero-dimensional MVNDiag object."""
      dummy_mvndiag = mvn_diag.MultivariateNormalDiag(
          scale_diag=tf.ones([0], dtype=dtype)
      )
      dummy_mvndiag.covariance = lambda: dummy_mvndiag.variance()[
          ..., tf.newaxis
      ]
      return dummy_mvndiag
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import linear_gaussian_ssm
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.sts.structural_time_series import Parameter
  from tensorflow_probability.python.sts.structural_time_series import StructuralTimeSeries
label_desc: Use image node_id 1 for calling a global function with example usage: _zero_dimensional_mvndiag(dtype) and returns: dummy_mvndiag