code: def broadcast_batch_shape(distributions):
      """Get broadcast batch shape from distributions, statically if possible."""
  
      # Static case
      batch_shape = distributions[0].batch_shape
      for distribution in distributions:
          batch_shape = tf.broadcast_static_shape(
              batch_shape, distribution.batch_shape
          )
      if tensorshape_util.is_fully_defined(batch_shape):
          return tensorshape_util.as_list(batch_shape)
  
      # Fallback on dynamic.
      batch_shape = distributions[0].batch_shape_tensor()
      for distribution in distributions:
          batch_shape = tf.broadcast_dynamic_shape(
              batch_shape, distribution.batch_shape_tensor()
          )
  
      return tf.convert_to_tensor(value=batch_shape)
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import mvn_linear_operator
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.sts.internal import missing_values_util
label_desc: Use image node_id 1 for calling a global function with example usage: broadcast_batch_shape(distributions) and returns: tf, tensorshape_util