code: def segment_min_or_constant(
      data: jnp.ndarray,
      segment_ids: jnp.ndarray,
      num_segments: Optional[int] = None,
      indices_are_sorted: bool = False,
      unique_indices: bool = False,
      constant: float = 0.0,
  ):
      """As segment_min, but returns a constant for empty segments.
  
      `segment_min` returns `-inf` for empty segments, which can cause `nan`s in the
      backwards pass of a neural network, even with masking. This method overrides
      the default behaviour of `segment_min` and returns a constant for empty
      segments.
  
      Args:
        data: an array with the values to be maxed over.
        segment_ids: an array with integer dtype that indicates the segments of
          `data` (along its leading axis) to be maxed over. Values can be repeated
          and need not be sorted. Values outside of the range [0, num_segments) are
          dropped and do not contribute to the result.
        num_segments: optional, an int with positive value indicating the number of
          segments. The default is ``jnp.maximum(jnp.max(segment_ids) + 1,
          jnp.max(-segment_ids))`` but since `num_segments` determines the size of
          the output, a static value must be provided to use ``segment_min`` in a
          ``jit``-compiled function.
        indices_are_sorted: whether ``segment_ids`` is known to be sorted
        unique_indices: whether ``segment_ids`` is known to be free of duplicates
        constant: The constant to replace empty segments with, defaults to zero.
  
      Returns:
        An array with shape ``(num_segments,) + data.shape[1:]`` representing
        the segment maxs.
      """
      mins_ = segment_min(
          data,
          segment_ids,
          num_segments,
          indices_are_sorted,
          unique_indices,
      )
      return _replace_empty_segments_with_constant(
          mins_, segment_ids, num_segments, constant
      )
  
imports: import functools
  from typing import Any, Callable, Generator, Iterable, Iterator, List, Mapping, Optional, Sequence, Union
  import jax
  from jax import lax
  import jax.numpy
  import jax.tree_util
  from jraph._src import graph
  import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: segment_min_or_constant(data, segment_ids, num_segments, indices_are_sorted, unique_indices, constant) and returns: _replace_empty_segments_with_constant