code: def _broadcast_cat_event_and_params(event, params, base_dtype):
      """Broadcasts the event or distribution parameters."""
      if dtype_util.is_integer(event.dtype):
          pass
      elif dtype_util.is_floating(event.dtype):
          # When `validate_args=True` we've already ensured int/float casting
          # is closed.
          event = tf.cast(event, dtype=tf.int32)
      else:
          raise TypeError(
              "`value` should have integer `dtype` or "
              "`self.dtype` ({})".format(base_dtype)
          )
      shape_known_statically = (
          tensorshape_util.rank(params.shape) is not None
          and tensorshape_util.is_fully_defined(params.shape[:-1])
          and tensorshape_util.is_fully_defined(event.shape)
      )
      if not shape_known_statically or params.shape[:-1] != event.shape:
          params = params * tf.ones_like(
              event[..., tf.newaxis], dtype=params.dtype
          )
          params_shape = ps.shape(params)[:-1]
          event = event * tf.ones(params_shape, dtype=event.dtype)
          if tensorshape_util.rank(params.shape) is not None:
              tensorshape_util.set_shape(event, params.shape[:-1])
  
      return event, params
  
imports: import numpy
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import softmax_centered
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 1 for calling a global function with example usage: _broadcast_cat_event_and_params(event, params, base_dtype) and returns: event,  params