code: def _get_monotonic_chunk_index_min_max(index, index_chunks):
      chunk_index_min_max = _get_chunk_index_min_max(index_chunks)
      if index.is_monotonic_decreasing:
          return list(reversed(chunk_index_min_max)), False
  
      for j in range(len(chunk_index_min_max) - 1):
          # overlap only if the prev max is close and curr min is close
          # and they are identical
          prev_max, prev_max_close = chunk_index_min_max[j][2:]
          curr_min, curr_min_close = chunk_index_min_max[j + 1][:2]
          if prev_max_close and curr_min_close and prev_max == curr_min:
              return
      return chunk_index_min_max, True
  
imports: import itertools
  import operator
  import numpy
  import pandas
  from ..None import opcodes
  from ..core import OutputType
  from ..core.operand import OperandStage, MapReduceOperand
  from ..serialization.serializables import FieldTypes, AnyField, BoolField, Int32Field, KeyField, ListField
  from .core import SERIES_CHUNK_TYPE
  from .utils import hash_dtypes, filter_dtypes
  from .operands import DataFrameOperandMixin, DataFrameShuffleProxy
  from .utils import parse_index, split_monotonic_index_min_max, build_split_idx_to_origin_idx, filter_index_value, hash_index, is_index_value_identical, validate_axis
label_desc: Use image node_id 2 for calling a global function with example usage: _get_monotonic_chunk_index_min_max(index, index_chunks) and returns: chunk_index_min_max,  True, list,  False