code: def setitem(x, v, indices):
      """Chunk function of `setitem_array`.
  
      Assign v to indices of x.
  
      Parameters
      ----------
      x : numpy array
          The array to be assigned to.
      v : numpy array
          The values which will be assigned.
      indices : list of `slice`, `int`, or numpy array
          The indices describing the elements of x to be assigned from
          v. One index per axis.
  
          Note that an individual index can not be a `list`, use a 1-d
          numpy array instead.
  
          If a 1-d numpy array index contains the non-valid value of the
          size of the corresponding dimension of x, then those index
          elements will be removed prior to the assignment (see
          `block_index_from_1d_index` function).
  
      Returns
      -------
      numpy array
          A new independent array with assigned elements, unless v is
          empty (i.e. has zero size) in which case then the input array
          is returned and the indices are ignored.
  
      Examples
      --------
      >>> x = np.arange(8).reshape(2, 4)
      >>> setitem(x, np.array(-99), [np.array([False, True])])
      array([[  0,   1,   2,   3],
             [-99, -99, -99, -99]])
      >>> x
      array([[0, 1, 2, 3],
             [4, 5, 6, 7]])
      >>> setitem(x, np.array([-88, -99]), [slice(None), np.array([1, 3])])
      array([[  0, -88,   2, -99],
             [  4, -88,   6, -99]])
      >>> setitem(x, -x, [slice(None)])
      array([[ 0, -1, -2, -3],
             [-4, -5, -6, -7]])
      >>> x
      array([[0, 1, 2, 3],
             [4, 5, 6, 7]])
      >>> setitem(x, np.array([-88, -99]), [slice(None), np.array([4, 4, 3, 4, 1, 4])])
      array([[  0, -99,   2, -88],
             [  4, -99,   6, -88]])
      >>> value = np.where(x < 0)[0]
      >>> value.size
      0
      >>> y = setitem(x, value, [Ellipsis])
      >>> y is x
      True
      """
      if not v.size:
          return x
  
      # Normalize integer array indices
      for i, (index, block_size) in enumerate(zip(indices, x.shape)):
          if isinstance(index, np.ndarray) and index.dtype != bool:
              # Strip out any non-valid place-holder values
              index = index[np.where(index < block_size)[0]]
              indices[i] = index
  
      # If x is not masked but v is, then turn the x into a masked
      # array.
      if not np.ma.isMA(x) and np.ma.isMA(v):
          x = x.view(np.ma.MaskedArray)
  
      # Copy the array to guarantee no other objects are corrupted
      x = x.copy()
  
      # Do the assignment
      try:
          x[tuple(indices)] = v
      except ValueError as e:
          raise ValueError(
              "shape mismatch: value array could "
              "not be broadcast to indexing result"
          ) from e
  
      return x
  
imports: from __future__ import annotations
  import bisect
  import functools
  import math
  import warnings
  from itertools import product
  from numbers import Integral, Number
  from operator import itemgetter
  import numpy
  from tlz import concat, memoize, merge, pluck
  from dask import config, core, utils
  from dask.array.chunk import getitem
  from dask.base import is_dask_collection, tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import cached_cumsum, is_arraylike
label_desc: Use image node_id 29 for calling a global function with example usage: setitem(x, v, indices) and returns: x, x