code: def _segment_update(
      name: str,
      data: ArrayLike,
      segment_ids: ArrayLike,
      scatter_op: Callable,
      num_segments: int | None = None,
      indices_are_sorted: bool = False,
      unique_indices: bool = False,
      bucket_size: int | None = None,
      reducer: Callable | None = None,
      mode: lax.GatherScatterMode | None = None,
  ) -> Array:
      check_arraylike(name, data, segment_ids)
      mode = (
          lax.GatherScatterMode.FILL_OR_DROP if mode is None else mode
      )
      data = jnp.asarray(data)
      segment_ids = jnp.asarray(segment_ids)
      dtype = data.dtype
      if num_segments is None:
          num_segments = np.max(segment_ids) + 1
      num_segments = core.concrete_dim_or_error(
          num_segments, "segment_sum() `num_segments` argument."
      )
      if num_segments is not None and num_segments < 0:
          raise ValueError("num_segments must be non-negative.")
  
      if bucket_size is None:
          out = jnp.full(
              (num_segments,) + data.shape[1:],
              _get_identity(scatter_op, dtype),
              dtype=dtype,
          )
          return _scatter_update(
              out,
              segment_ids,
              data,
              scatter_op,
              indices_are_sorted,
              unique_indices,
              normalize_indices=False,
              mode=mode,
          )
  
      # Bucketize indices and perform segment_update on each bucket to improve
      # numerical stability for operations like product and sum.
      assert reducer is not None
      num_buckets = util.ceil_of_ratio(segment_ids.size, bucket_size)
      out = jnp.full(
          (num_buckets, num_segments) + data.shape[1:],
          _get_identity(scatter_op, dtype),
          dtype=dtype,
      )
      out = _scatter_update(
          out,
          np.index_exp[
              jnp.arange(segment_ids.shape[0]) // bucket_size,
              segment_ids[None, :],
          ],
          data,
          scatter_op,
          indices_are_sorted,
          unique_indices,
          normalize_indices=False,
          mode=mode,
      )
      return reducer(out, axis=0).astype(dtype)
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import sys
  from typing import Callable, Union
  import warnings
  import numpy
  from jax import lax
  from jax._src import config
  from jax._src import core
  from jax._src import dtypes
  from jax._src import util
  from jax._src.lax import lax
  from jax._src.numpy import lax_numpy
  from jax._src.numpy import reductions
  from jax._src.numpy.util import check_arraylike, promote_dtypes
  from jax._src.typing import Array, ArrayLike
label_desc: Use image node_id 4 for calling a global function with example usage: _segment_update(name, data, segment_ids, scatter_op, num_segments, indices_are_sorted, unique_indices, bucket_size, reducer, mode) and returns: reducer, _scatter_update