code: def tile(cls, op):
      in_series = op.inputs[0]
      out_series = op.outputs[0]
  
      if len(in_series.chunks) == 1:
          return cls._tile_one_chunk(op)
      if not in_series.index_value.has_value():
          return cls._tree_getitem(op)
  
      chunk_indexes = [
          c.index_value.to_pandas() for c in in_series.chunks
      ]
      if not isinstance(op.labels, list):
          selected_chunk = in_series.chunks[
              cls._calc_chunk_index(op.labels, chunk_indexes)
          ]
          index_op = op.copy().reset_key()
          out_chunk = index_op.new_chunk(
              [selected_chunk], shape=(), dtype=selected_chunk.dtype
          )
          new_op = op.copy()
          return new_op.new_scalars(
              op.inputs, dtype=out_series.dtype, chunks=[out_chunk]
          )
      else:
          # When input series's index is RangeIndex(5), chunk_size is 3, and labels is [4, 2, 3, 4],
          # Combine the labels in the same chunk, so the splits will be [[4], [2], [3, 4]],
          # the corresponding chunk index is [1, 0, 1].
          selected_index = [
              cls._calc_chunk_index(label, chunk_indexes)
              for label in op.labels
          ]
          condition = np.where(np.diff(selected_index))[0] + 1
          column_splits = np.split(op.labels, condition)
          column_indexes = np.split(selected_index, condition)
  
          out_chunks = []
          nsplits = []
          for i, (labels, idx) in enumerate(
              zip(column_splits, column_indexes)
          ):
              index_op = SeriesIndex(labels=list(labels))
              c = in_series.chunks[idx[0]]
              nsplits.append(len(labels))
              index_value = parse_index(
                  pd.Index([], dtype=c.index_value.to_pandas().dtype),
                  c,
                  labels,
              )
              out_chunks.append(
                  index_op.new_chunk(
                      [c],
                      shape=(len(labels),),
                      dtype=c.dtype,
                      index_value=index_value,
                      name=c.name,
                      index=(i,),
                  )
              )
          new_op = op.copy()
          return new_op.new_seriess(
              op.inputs,
              shape=out_series.shape,
              dtype=out_series.dtype,
              index_value=out_series.index_value,
              nsplits=(tuple(nsplits),),
              chunks=out_chunks,
              name=out_series.name,
          )
  
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 11 for calling the SeriesIndex obj's underlying member method code with example usage: obj.tile(cls, op) and returns: cls, cls, new_op, new_op