code: def _gen_tree_chunks(cls, op: "DuplicateOperand", inp, method):
      from ..merge import DataFrameConcat
  
      out = op.outputs[0]
      combine_size = options.combine_size
      new_chunks = cls._gen_map_chunks(op, inp, method)
      while len(new_chunks) > 1:
          out_chunk_size = ceildiv(len(new_chunks), combine_size)
          out_chunks = []
          for i in range(out_chunk_size):
              in_chunks = new_chunks[
                  i * combine_size : (i + 1) * combine_size
              ]
              s = sum(c.shape[0] for c in in_chunks)
              if in_chunks[0].ndim == 2:
                  kw = dict(
                      dtypes=in_chunks[0].dtypes,
                      index_value=in_chunks[0].index_value,
                      columns_value=in_chunks[0].columns_value,
                      shape=(s, in_chunks[0].shape[1]),
                      index=(i, 0),
                  )
              else:
                  kw = dict(
                      dtype=in_chunks[0].dtype,
                      index_value=in_chunks[0].index_value,
                      name=in_chunks[0].name,
                      shape=(s,),
                      index=(i,),
                  )
              concat_chunk = DataFrameConcat(
                  output_types=in_chunks[0].op.output_types
              ).new_chunk(in_chunks, **kw)
              chunk_op = op.copy().reset_key()
              chunk_op._method = method
              chunk_op.stage = (
                  OperandStage.combine
                  if out_chunk_size > 1
                  else OperandStage.agg
              )
              if out_chunk_size > 1 and method == "tree":
                  # for tree, chunks except last one should be dataframes,
                  chunk_op._output_types = (
                      concat_chunk.op.output_types
                      if out_chunk_size > 1
                      else out.op.output_types
                  )
              elif method == "subset_tree":
                  # `subset_tree` will tile chunks that are always dataframes
                  chunk_op._output_types = [OutputType.dataframe]
              params = cls._gen_chunk_params(chunk_op, concat_chunk)
              if out.ndim == 1 and out_chunk_size == 1:
                  params["name"] = out.name
              out_chunks.append(
                  chunk_op.new_chunk([concat_chunk], kws=[params])
              )
          new_chunks = out_chunks
  
      return new_chunks
  
imports: import inspect
  import numpy
  import pandas
  from pandas.api.types import is_list_like
  from ...config import options
  from ...core import OutputType, recursive_tile
  from ...core.operand import OperandStage, MapReduceOperand
  from ...serialization.serializables import AnyField, Int32Field, StringField, KeyField
  from ...utils import ceildiv, has_unknown_shape, lazy_import
  from ..initializer import DataFrame
  from ..operands import DataFrameOperandMixin, DataFrameShuffleProxy
label_desc: Use image node_id 14 for calling the DuplicateOperand obj's underlying member method code with example usage: obj._gen_tree_chunks(cls, op, inp, method) and returns: new_chunks