code: def create_tileable_from_chunks(cls, chunks, inputs=None, **kw):
      ndim = chunks[0].ndim
      index_min, index_max = [None] * ndim, [None] * ndim
      for c in chunks:
          for ax, i in enumerate(c.index):
              if index_min[ax] is None:
                  index_min[ax] = i
              else:
                  index_min[ax] = min(i, index_min[ax])
              if index_max[ax] is None:
                  index_max[ax] = i
              else:
                  index_max[ax] = max(i, index_max[ax])
  
      # gen {chunk index -> shape}
      chunk_index_to_shape = OrderedDict()
      chunk_index_to_chunk = dict()
      for c in chunks:
          new_index = []
          for ax, i in enumerate(c.index):
              new_index.append(i - index_min[ax])
          chunk_index_to_shape[tuple(new_index)] = c.shape
          chunk_index_to_chunk[tuple(new_index)] = c
  
      nsplits = calc_nsplits(chunk_index_to_shape)
      shape = tuple(sum(ns) for ns in nsplits)
      chunk_shape = tuple(len(ns) for ns in nsplits)
      op = chunks[0].op.copy().reset_key()
      if isinstance(chunks[0], DATAFRAME_CHUNK_TYPE):
          params = cls._calc_dataframe_params(
              chunk_index_to_chunk, chunk_shape
          )
          params.update(kw)
          return op.new_dataframe(
              inputs,
              shape=shape,
              chunks=chunks,
              nsplits=nsplits,
              **params,
          )
      elif isinstance(chunks[0], SERIES_CHUNK_TYPE):
          params = cls._calc_series_index_params(chunks)
          params.update(kw)
          return op.new_series(
              inputs,
              shape=shape,
              chunks=chunks,
              nsplits=nsplits,
              **params,
          )
      else:
          assert isinstance(chunks[0], INDEX_CHUNK_TYPE)
          params = cls._calc_series_index_params(chunks)
          params.update(kw)
          return op.new_index(
              inputs,
              shape=shape,
              chunks=chunks,
              nsplits=nsplits,
              **params,
          )
  
imports: from collections import OrderedDict
  from functools import reduce
  import numpy
  import pandas
  from ..core import FuseChunkData, FuseChunk, ENTITY_TYPE, OutputType
  from ..core.operand import Operand, TileableOperandMixin, Fuse, ShuffleProxy, FuseChunkMixin
  from ..tensor.core import TENSOR_TYPE
  from ..tensor.datasource import tensor
  from ..tensor.operands import TensorOperandMixin
  from ..utils import calc_nsplits
  from .core import DATAFRAME_CHUNK_TYPE, SERIES_CHUNK_TYPE, INDEX_CHUNK_TYPE, DATAFRAME_TYPE, SERIES_TYPE, INDEX_TYPE, DATAFRAME_GROUPBY_TYPE, SERIES_GROUPBY_TYPE, CATEGORICAL_TYPE
  from .utils import parse_index
label_desc: Use image node_id 15 for calling the DataFrameOperandMixin obj's underlying member method code with example usage: obj.create_tileable_from_chunks(cls, chunks, inputs) and returns: op, op, op