code: def test_dynamic_shape(self):
      x = tf.Variable(ps.ones([7, 3]), shape=[7, None])
      self.evaluate(x.initializer)
  
      # Check that the shape is actually `None`.
      if not tf.executing_eagerly():
          last_shape = x.shape[-1]
          if last_shape is not None:  # This is a `tf.Dimension` in tf1.
              last_shape = last_shape.value
          self.assertIsNone(last_shape)
      dynamic_dist = (
          mvnpflo.MultivariateNormalPrecisionFactorLinearOperator(
              precision_factor=tf.linalg.LinearOperatorDiag(
                  tf.ones_like(x)
              )
          )
      )
      static_dist = (
          mvnpflo.MultivariateNormalPrecisionFactorLinearOperator(
              precision_factor=tf.linalg.LinearOperatorDiag(
                  tf.ones([7, 3])
              )
          )
      )
      in_ = tf.zeros([7, 3])
      self.assertAllClose(
          self.evaluate(dynamic_dist.log_prob(in_)),
          static_dist.log_prob(in_),
      )
  
file_path: probability/tensorflow_probability/python/experimental/distributions/mvn_precision_factor_linop_test.py
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.experimental.distributions import mvn_precision_factor_linop
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_combinations
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 3 for calling the MVNPrecisionFactorLinOpTest obj's underlying member method code with example usage: obj.test_dynamic_shape() without return types