code: def test_event_space_bijector(self):
      # Test that the default event space bijector executes.  This is
      # non-trivial, because the event space bijector of this particular
      # component distribution cannot be relied upon to produce finite
      # values in the unconstrained space from samples of `sub_d`.
      sub_d = relaxed_onehot_categorical.ExpRelaxedOneHotCategorical(
          logits=[0.0, 0.0, 0.0],
          temperature=[0.01, 0.01, 0.01, 0.01],
          validate_args=True,
      )
      d = masked.Masked(sub_d, validity_mask=False, validate_args=True)
      bij = d.experimental_default_event_space_bijector()
      x = bij(tf.zeros(shape=[4, 2]))
      # The error tested for manifests as failed validations due to
      # invalid values.
      self.assertAllNotNan(self.evaluate(x))
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import masked
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import relaxed_onehot_categorical
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 3 for calling the MaskedTest obj's underlying member method code with example usage: obj.test_event_space_bijector() without return types