code: def _get_data_chunks_workers(ctx, data):
      # data_chunk.inputs is concat, and concat's input is the co-allocated chunks
      metas = ctx.get_chunks_meta(
          [c.key for c in data.chunks], fields=["ip", "bands"]
      )
  
      ips = []
      ip_to_worker = {}
      for m in metas:
          ip = m["ip"]
          assert ip, "There is meta {meta} who doesn't contain ip."
          ips.append(ip)
          bands = m["bands"]
          ip_to_worker[ip] = bands[0][0] if bands else None
      return ips, ip_to_worker
  
imports: import itertools
  import logging
  import operator
  import pickle
  from collections import defaultdict
  from functools import reduce
  import numpy
  from ....None import opcodes
  from ....core import ExecutableTuple, OutputType, recursive_tile
  from ....core.context import get_context
  from ....core.operand import MergeDictOperand
  from ....serialization.serializables import FieldTypes, DictField, Int32Field, KeyField, ListField, StringField
  from ...utils import concat_chunks, collect_ports
  from ._align import align_data_set
  from .core import LGBMModelType, get_model_cls_from_type
label_desc: Use image node_id 20 for calling the LGBMTrain obj's underlying member method code with example usage: obj._get_data_chunks_workers(ctx, data) and returns: ips,  ip_to_worker