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)
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`.
  
graph_nets/blocks.py