class_docstr: A JDMixin that shards the log_prob calculation.
code: def _call_execute_model(
      self,
      sample_shape=(),
      seed=None,
      value=None,
      sample_and_trace_fn=jd_lib.trace_distributions_and_values,
  ):
      return self._distribute_execute_model(
          sample_shape=sample_shape,
          seed=seed,
          value=value if value is None else self._model_flatten(value),
          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 JointDistributionDistributedMixin obj's underlying member method code with example usage: obj._call_execute_model(sample_shape, seed, value, sample_and_trace_fn) and returns: self