docstr: return the join indexers 
code: def _get_join_indexers(self):
      """return the join indexers"""
  
      def flip(xs):
          """unlike np.transpose, this returns an array of tuples"""
          labels = list(string.ascii_lowercase[: len(xs)])
          dtypes = [x.dtype for x in xs]
          labeled_dtypes = list(zip(labels, dtypes))
          return np.array(lzip(*xs), labeled_dtypes)
  
      # values to compare
      left_values = (
          self.left.index.values
          if self.left_index
          else self.left_join_keys[-1]
      )
      right_values = (
          self.right.index.values
          if self.right_index
          else self.right_join_keys[-1]
      )
      tolerance = self.tolerance
  
      # we require sortedness and non-null values in the join keys
      msg_sorted = "{side} keys must be sorted"
      msg_missings = "Merge keys contain null values on {side} side"
  
      if not Index(left_values).is_monotonic:
          if isnull(left_values).any():
              raise ValueError(msg_missings.format(side="left"))
          else:
              raise ValueError(msg_sorted.format(side="left"))
  
      if not Index(right_values).is_monotonic:
          if isnull(right_values).any():
              raise ValueError(msg_missings.format(side="right"))
          else:
              raise ValueError(msg_sorted.format(side="right"))
  
      # initial type conversion as needed
      if needs_i8_conversion(left_values):
          left_values = left_values.view("i8")
          right_values = right_values.view("i8")
          if tolerance is not None:
              tolerance = tolerance.value
  
      # a "by" parameter requires special handling
      if self.left_by is not None:
          # remove 'on' parameter from values if one existed
          if self.left_index and self.right_index:
              left_by_values = self.left_join_keys
              right_by_values = self.right_join_keys
          else:
              left_by_values = self.left_join_keys[0:-1]
              right_by_values = self.right_join_keys[0:-1]
  
          # get tuple representation of values if more than one
          if len(left_by_values) == 1:
              left_by_values = left_by_values[0]
              right_by_values = right_by_values[0]
          else:
              left_by_values = flip(left_by_values)
              right_by_values = flip(right_by_values)
  
          # upcast 'by' parameter because HashTable is limited
          by_type = _get_cython_type_upcast(left_by_values.dtype)
          by_type_caster = _type_casters[by_type]
          left_by_values = by_type_caster(left_by_values)
          right_by_values = by_type_caster(right_by_values)
  
          # choose appropriate function by type
          func = _asof_by_function(self.direction)
          return func(
              left_values,
              right_values,
              left_by_values,
              right_by_values,
              self.allow_exact_matches,
              tolerance,
          )
      else:
          # choose appropriate function by type
          func = _asof_function(self.direction)
          return func(
              left_values,
              right_values,
              self.allow_exact_matches,
              tolerance,
          )
  
imports: import copy
  import string
  import warnings
  import numpy
  from pandas._libs import hashtable, join, lib
  import pandas.compat
  from pandas.compat import filter, lzip, map, range, zip
  from pandas.errors import MergeError
  from pandas.util._decorators import Appender, Substitution
  from pandas.core.dtypes.common import ensure_float64, ensure_int64, ensure_object, is_array_like, is_bool, is_bool_dtype, is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_datetimelike, is_dtype_equal, is_extension_array_dtype, is_float_dtype, is_int64_dtype, is_integer, is_integer_dtype, is_list_like, is_number, is_numeric_dtype, is_object_dtype, needs_i8_conversion
  from pandas.core.dtypes.missing import isnull, na_value_for_dtype
  from pandas import Categorical, DataFrame, Index, MultiIndex, Series, Timedelta
  import pandas.core.algorithms
  from pandas.core.arrays.categorical import _recode_for_categories
  import pandas.core.common
  from pandas.core.frame import _merge_doc
  from pandas.core.internals import concatenate_block_managers, items_overlap_with_suffix
  import pandas.core.sorting
  from pandas.core.sorting import is_int64_overflow_possible
label_desc: Use image node_id 5 for calling the _AsOfMerge obj's underlying member method code with example usage: obj._get_join_indexers() and returns: np, func, func