code: def segment_variance(
      data: jnp.ndarray,
      segment_ids: jnp.ndarray,
      num_segments: Optional[int] = None,
      indices_are_sorted: bool = False,
      unique_indices: bool = False,
  ):
      """Returns the variance for each segment.
  
      Args:
        data: values whose variance will be calculated segment-wise.
        segment_ids: indices for segments
        num_segments: total number of segments.
        indices_are_sorted: whether ``segment_ids`` is known to be sorted.
        unique_indices: whether ``segment_ids`` is known to be free of duplicates.
  
      Returns:
        num_segments size array containing the variance of each segment.
      """
      means = segment_mean(
          data,
          segment_ids,
          num_segments,
          indices_are_sorted=indices_are_sorted,
          unique_indices=unique_indices,
      )[segment_ids]
      counts = segment_sum(
          jnp.ones_like(data),
          segment_ids,
          num_segments,
          indices_are_sorted=indices_are_sorted,
          unique_indices=unique_indices,
      )
      counts = jnp.maximum(counts, jnp.ones_like(counts))
      variances = (
          segment_sum(
              jnp.power(data - means, 2),
              segment_ids,
              num_segments,
              indices_are_sorted=indices_are_sorted,
              unique_indices=unique_indices,
          )
          / counts
      )
      return variances
  
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 3 for calling a global function with example usage: segment_variance(data, segment_ids, num_segments, indices_are_sorted, unique_indices) and returns: variances