code: def slice_with_bool_dask_array(x, index):
      """Slice x with one or more dask arrays of bools
  
      This is a helper function of `Array.__getitem__`.
  
      Parameters
      ----------
      x: Array
      index: tuple with as many elements as x.ndim, among which there are
             one or more Array's with dtype=bool
  
      Returns
      -------
      tuple of (sliced x, new index)
  
      where the new index is the same as the input, but with slice(None)
      replaced to the original slicer when a filter has been applied.
  
      Note: The sliced x will have nan chunks on the sliced axes.
      """
      from dask.array.core import Array, blockwise, elemwise
  
      out_index = [
          slice(None)
          if isinstance(ind, Array) and ind.dtype == bool
          else ind
          for ind in index
      ]
  
      if len(index) == 1 and index[0].ndim == x.ndim:
          if (
              not np.isnan(x.shape).any()
              and not np.isnan(index[0].shape).any()
          ):
              x = x.ravel()
              index = tuple(i.ravel() for i in index)
          elif x.ndim > 1:
              warnings.warn(
                  "When slicing a Dask array of unknown chunks with a boolean mask "
                  "Dask array, the output array may have a different ordering "
                  "compared to the equivalent NumPy operation. This will raise an "
                  "error in a future release of Dask.",
                  stacklevel=3,
              )
          y = elemwise(getitem, x, *index, dtype=x.dtype)
          name = "getitem-" + tokenize(x, index)
          dsk = {
              (name, i): k
              for i, k in enumerate(core.flatten(y.__dask_keys__()))
          }
          chunks = ((np.nan,) * y.npartitions,)
          graph = HighLevelGraph.from_collections(
              name, dsk, dependencies=[y]
          )
          return Array(graph, name, chunks, x.dtype), out_index
  
      if any(
          isinstance(ind, Array) and ind.dtype == bool and ind.ndim != 1
          for ind in index
      ):
          raise NotImplementedError(
              "Slicing with dask.array of bools only permitted when "
              "the indexer has only one dimension or when "
              "it has the same dimension as the sliced "
              "array"
          )
      indexes = [
          ind
          if isinstance(ind, Array) and ind.dtype == bool
          else slice(None)
          for ind in index
      ]
  
      arginds = []
      i = 0
      for ind in indexes:
          if isinstance(ind, Array) and ind.dtype == bool:
              new = (ind, tuple(range(i, i + ind.ndim)))
              i += x.ndim
          else:
              new = (slice(None), None)
              i += 1
          arginds.append(new)
  
      arginds = list(concat(arginds))
  
      out = blockwise(
          getitem_variadic,
          tuple(range(x.ndim)),
          x,
          tuple(range(x.ndim)),
          *arginds,
          dtype=x.dtype,
      )
  
      chunks = []
      for ind, chunk in zip(index, out.chunks):
          if isinstance(ind, Array) and ind.dtype == bool:
              chunks.append((np.nan,) * len(chunk))
          else:
              chunks.append(chunk)
      out._chunks = tuple(chunks)
      return out, tuple(out_index)
  
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 22 for calling a global function with example usage: slice_with_bool_dask_array(x, index) and returns: out,  tuple, Array,  out_index