code: def pbroadcast_value(value, value_axis_names, output_axis_names):
      value_axis_names = distribute_lib.canonicalize_named_axis(
          value_axis_names
      )
      pbroadcast_axes = [
          axis_name
          for axis_name in output_axis_names
          if axis_name not in value_axis_names
      ]
      return distribute_lib.pbroadcast(
          value, named_axis=pbroadcast_axes
      )
  
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 a global function with example usage: pbroadcast_value(value, value_axis_names, output_axis_names) and returns: distribute_lib