code: def unsorted_segment_min_or_zero(
      values, indices, num_groups, name="unsorted_segment_min_or_zero"
  ):
      """Aggregates information using elementwise min.
  
      Segments with no elements are given a "min" of zero instead of the most
      positive finite value possible (which is what `tf.math.unsorted_segment_min`
      would do).
  
      Args:
        values: A `Tensor` of per-element features.
        indices: A 1-D `Tensor` whose length is equal to `values`' first dimension.
        num_groups: A `Tensor`.
        name: (string, optional) A name for the operation.
  
      Returns:
        A `Tensor` of the same type as `values`.
      """
      with tf.name_scope(name):
          return _unsorted_segment_reduction_or_zero(
              tf.math.unsorted_segment_min, values, indices, num_groups
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from graph_nets import _base
  from graph_nets import graphs
  from graph_nets import utils_tf
  import tensorflow
label_desc: Use image node_id 10 for calling a global function with example usage: unsorted_segment_min_or_zero(values, indices, num_groups, name) and returns: _unsorted_segment_reduction_or_zero