code: def meshgrid(*xi, sparse=False, indexing="xy", **kwargs):
      sparse = bool(sparse)
  
      if "copy" in kwargs:
          raise NotImplementedError("`copy` not supported")
  
      if kwargs:
          raise TypeError("unsupported keyword argument(s) provided")
  
      if indexing not in ("ij", "xy"):
          raise ValueError("`indexing` must be `'ij'` or `'xy'`")
  
      xi = [asarray(e) for e in xi]
      xi = [e.flatten() for e in xi]
  
      if indexing == "xy" and len(xi) > 1:
          xi[0], xi[1] = xi[1], xi[0]
  
      grid = []
      for i in range(len(xi)):
          s = len(xi) * [None]
          s[i] = slice(None)
          s = tuple(s)
  
          r = xi[i][s]
  
          grid.append(r)
  
      if not sparse:
          grid = broadcast_arrays(*grid)
  
      if indexing == "xy" and len(xi) > 1:
          grid[0], grid[1] = grid[1], grid[0]
  
      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 9 for calling a global function with example usage: meshgrid() and returns: grid