code: def concat_tileable_chunks(cls, tileable):
      from .merge.concat import DataFrameConcat, GroupByConcat
  
      df = tileable
      assert not df.is_coarse()
  
      if isinstance(df, DATAFRAME_TYPE):
          chunk = DataFrameConcat(
              output_types=[OutputType.dataframe]
          ).new_chunk(
              df.chunks,
              shape=df.shape,
              index=(0, 0),
              dtypes=df.dtypes,
              index_value=df.index_value,
              columns_value=df.columns_value,
          )
          return DataFrameConcat(
              output_types=[OutputType.dataframe]
          ).new_dataframe(
              [df],
              shape=df.shape,
              chunks=[chunk],
              nsplits=tuple((s,) for s in df.shape),
              dtypes=df.dtypes,
              index_value=df.index_value,
              columns_value=df.columns_value,
          )
      elif isinstance(df, SERIES_TYPE):
          chunk = DataFrameConcat(
              output_types=[OutputType.series]
          ).new_chunk(
              df.chunks,
              shape=df.shape,
              index=(0,),
              dtype=df.dtype,
              index_value=df.index_value,
              name=df.name,
          )
          return DataFrameConcat(
              output_types=[OutputType.series]
          ).new_series(
              [df],
              shape=df.shape,
              chunks=[chunk],
              nsplits=tuple((s,) for s in df.shape),
              dtype=df.dtype,
              index_value=df.index_value,
              name=df.name,
          )
      elif isinstance(df, INDEX_TYPE):
          chunk = DataFrameConcat(
              output_types=[OutputType.index]
          ).new_chunk(
              df.chunks,
              shape=df.shape,
              index=(0,),
              dtype=df.dtype,
              index_value=df.index_value,
              name=df.name,
          )
          return DataFrameConcat(
              output_types=[OutputType.index]
          ).new_index(
              [df],
              shape=df.shape,
              chunks=[chunk],
              nsplits=tuple((s,) for s in df.shape),
              dtype=df.dtype,
              index_value=df.index_value,
              name=df.name,
          )
      elif isinstance(
          df, (DATAFRAME_GROUPBY_TYPE, SERIES_GROUPBY_TYPE)
      ):
          output_type = (
              OutputType.dataframe_groupby
              if isinstance(df, DATAFRAME_GROUPBY_TYPE)
              else OutputType.series_groupby
          )
          groupby_params = cls._process_groupby_params(
              df.op.groupby_params
          )
          inputs, chunk_inputs = cls._get_groupby_inputs(
              df, groupby_params
          )
          chunk = GroupByConcat(
              groups=df.chunks,
              groupby_params=groupby_params,
              output_types=[output_type],
          ).new_chunk(chunk_inputs, **df.params)
          return GroupByConcat(
              groups=[df],
              groupby_params=df.op.groupby_params,
              output_types=[output_type],
          ).new_tileable(inputs, chunks=[chunk], **df.params)
      elif isinstance(df, CATEGORICAL_TYPE):
          chunk = DataFrameConcat(
              output_types=[OutputType.categorical]
          ).new_chunk(
              df.chunks,
              shape=df.shape,
              index=(0,),
              dtype=df.dtype,
              categories_value=df.categories_value,
          )
          return DataFrameConcat(
              output_types=[OutputType.categorical]
          ).new_categorical(
              [df],
              shape=df.shape,
              chunks=[chunk],
              nsplits=tuple((s,) for s in df.shape),
              dtype=df.dtype,
              categories_value=df.categories_value,
          )
      elif isinstance(df, TENSOR_TYPE):
          return TensorOperandMixin.concat_tileable_chunks(tileable)
      else:
          raise NotImplementedError
  
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 14 for calling the DataFrameOperandMixin obj's underlying member method code with example usage: obj.concat_tileable_chunks(cls, tileable) and returns: DataFrameConcat, DataFrameConcat, DataFrameConcat, GroupByConcat, DataFrameConcat, TensorOperandMixin