class_docstr: Sharding-compatible event space bijector for JDs.
code: def _conditioned_bijectors(self, samples, constrained=False):
      if samples is None:
          return self.bijectors
  
      def sample_and_trace_fn(dist, value, **_):
          bij = self._bijector_fn(dist)
          if bij is None:
              bij = identity_bijector.Identity()
  
          # If the RV is not yet constrained, transform it.
          value = value if constrained else bij.forward(value)
          return jd_lib.ValueWithTrace(value=value, traced=bij)
  
      return self._jd._call_execute_model(  # pylint: disable=protected-access
          sample_shape=(),
          value=samples,
          seed=samplers.zeros_seed(),
          sample_and_trace_fn=sample_and_trace_fn,
      )
  
imports: import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import joint_distribution
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.internal import auto_composite_tensor
  from tensorflow_probability.python.internal import distribute_lib
  from tensorflow_probability.python.internal import samplers
label_desc: Use image node_id 1 for calling the _DefaultJointBijector obj's underlying member method code with example usage: obj._conditioned_bijectors(samples, constrained) and returns: self, self, jd_lib