docstr: DataFrame doesn't store the index value except RangeIndex or specify `store=True` in `parse_index`,
  So we build a tree structure to avoid too much dependence for getitem node.
docstr_tok: ["DataFrame","does","n't","store","the","index","value","except","RangeIndex","or","specify","`","store=True","`","in","`","parse_index","`",",","So","we","build","a","tree","structure","to","avoid","too","much","dependence","for","getitem","node","."]
code: def _tree_getitem(cls, op):
      """
      DataFrame doesn't store the index value except RangeIndex or specify `store=True` in `parse_index`,
      So we build a tree structure to avoid too much dependence for getitem node.
      """
      out_series = op.outputs[0]
      combine_size = options.combine_size
      chunks = op.inputs[0].chunks
      while len(chunks) > combine_size:
          new_chunks = []
          for i in range(0, len(chunks), combine_size):
              chks = chunks[i : i + combine_size]
              if len(chks) == 1:
                  chk = chks[0]
              else:
                  concat_op = DataFrameConcat(
                      output_types=[OutputType.series]
                  )
                  chk = concat_op.new_chunk(chks, dtype=chks[0].dtype)
              chk_op = SeriesIndex(
                  labels=op.labels, is_intermediate=True
              )
              kw = (
                  {"name": out_series.name}
                  if hasattr(out_series, "name")
                  else {}
              )
              chk = chk_op.new_chunk(
                  [chk],
                  shape=(np.nan,),
                  dtype=chk.dtype,
                  index_value=parse_index(pd.RangeIndex(-1)),
                  **kw,
              )
              new_chunks.append(chk)
          chunks = new_chunks
  
      concat_op = DataFrameConcat(output_types=[OutputType.series])
      kw = (
          {"name": out_series.name}
          if hasattr(out_series, "name")
          else {}
      )
      kw["index"] = (0,)
      chk = concat_op.new_chunk(chunks, dtype=chunks[0].dtype, **kw)
      index_op = SeriesIndex(labels=op.labels)
      chunk = index_op.new_chunk([chk], dtype=chk.dtype, **kw)
      new_op = op.copy()
      nsplits = (
          ((len(op.labels),),) if isinstance(op.labels, list) else ()
      )
      kw = out_series.params
      kw["nsplits"] = nsplits
      kw["chunks"] = [chunk]
      return new_op.new_tileables(op.inputs, kws=[kw])
  
imports: import itertools
  from numbers import Integral
  import pandas
  import numpy
  from ...None import opcodes
  from ...config import options
  from ...core import ENTITY_TYPE, OutputType, recursive_tile
  from ...serialization.serializables import AnyField, Int32Field, BoolField
  from ...tensor.core import TENSOR_TYPE, TENSOR_CHUNK_TYPE
  from ...tensor.datasource import tensor
  from ...utils import has_unknown_shape
  from ..align import align_dataframe_series, align_dataframe_dataframe
  from ..core import SERIES_TYPE, SERIES_CHUNK_TYPE, DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE, is_chunk_meta_lazy
  from ..merge import DataFrameConcat
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, in_range_index
  from .utils import calc_columns_index
label_desc: Use image node_id 10 for calling the SeriesIndex obj's underlying member method code with example usage: obj._tree_getitem(cls, op) and returns: new_op