code: def get_indexer_dict(
      label_list: list[np.ndarray], keys: list[Index]
  ) -> dict[Hashable, npt.NDArray[np.intp]]:
      """
      Returns
      -------
      dict:
          Labels mapped to indexers.
      """
      shape = tuple(len(x) for x in keys)
  
      group_index = get_group_index(
          label_list, shape, sort=True, xnull=True
      )
      if np.all(group_index == -1):
          # Short-circuit, lib.indices_fast will return the same
          return {}
      ngroups = (
          ((group_index.size and group_index.max()) + 1)
          if is_int64_overflow_possible(shape)
          else np.prod(shape, dtype="i8")
      )
  
      sorter = get_group_index_sorter(group_index, ngroups)
  
      sorted_labels = [lab.take(sorter) for lab in label_list]
      group_index = group_index.take(sorter)
  
      return lib.indices_fast(sorter, group_index, keys, sorted_labels)
  
imports: from __future__ import annotations
  from collections import defaultdict
  from typing import TYPE_CHECKING, Callable, DefaultDict, Hashable, Iterable, Sequence, cast
  import numpy
  from pandas._libs import algos, hashtable, lib
  from pandas._libs.hashtable import unique_label_indices
  from pandas._typing import AxisInt, IndexKeyFunc, Level, NaPosition, Shape, SortKind, npt
  from pandas.core.dtypes.common import ensure_int64, ensure_platform_int, is_extension_array_dtype
  from pandas.core.dtypes.generic import ABCMultiIndex, ABCRangeIndex
  from pandas.core.dtypes.missing import isna
  from pandas.core.construction import extract_array
label_desc: Use image node_id 15 for calling a global function with example usage: get_indexer_dict(label_list, keys) and returns: lib, dict