code: def indices(dimensions, dtype=int, chunks="auto"):
      """
      Implements NumPy's ``indices`` for Dask Arrays.
  
      Generates a grid of indices covering the dimensions provided.
  
      The final array has the shape ``(len(dimensions), *dimensions)``. The
      chunks are used to specify the chunking for axis 1 up to
      ``len(dimensions)``. The 0th axis always has chunks of length 1.
  
      Parameters
      ----------
      dimensions : sequence of ints
          The shape of the index grid.
      dtype : dtype, optional
          Type to use for the array. Default is ``int``.
      chunks : sequence of ints, str
          The size of each block.  Must be one of the following forms:
  
          - A blocksize like (500, 1000)
          - A size in bytes, like "100 MiB" which will choose a uniform
            block-like shape
          - The word "auto" which acts like the above, but uses a configuration
            value ``array.chunk-size`` for the chunk size
  
          Note that the last block will have fewer samples if ``len(array) % chunks != 0``.
  
      Returns
      -------
      grid : dask array
      """
      dimensions = tuple(dimensions)
      dtype = np.dtype(dtype)
      chunks = normalize_chunks(chunks, shape=dimensions, dtype=dtype)
  
      if len(dimensions) != len(chunks):
          raise ValueError("Need same number of chunks as dimensions.")
  
      xi = []
      for i in range(len(dimensions)):
          xi.append(
              arange(dimensions[i], dtype=dtype, chunks=(chunks[i],))
          )
  
      grid = []
      if all(dimensions):
          grid = meshgrid(*xi, indexing="ij")
  
      if grid:
          grid = stack(grid)
      else:
          grid = empty(
              (len(dimensions),) + dimensions,
              dtype=dtype,
              chunks=(1,) + chunks,
          )
  
      return grid
  
imports: from __future__ import annotations
  import itertools
  from collections.abc import Sequence
  from functools import partial
  from itertools import product
  from numbers import Integral, Number
  import numpy
  from tlz import sliding_window
  from dask.array import chunk
  from dask.array.backends import array_creation_dispatch
  from dask.array.core import Array, asarray, block, blockwise, broadcast_arrays, broadcast_to, concatenate, normalize_chunks, stack
  from dask.array.numpy_compat import AxisError
  from dask.array.ufunc import greater_equal, rint
  from dask.array.utils import meta_from_array
  from dask.array.wrap import empty, full, ones, zeros
  from dask.base import tokenize
  from dask.highlevelgraph import HighLevelGraph
  from dask.utils import cached_cumsum, derived_from, is_cupy_type
label_desc: Use image node_id 10 for calling a global function with example usage: indices(dimensions, dtype, chunks) and returns: grid