code: def tile(cls, op: "DataFrameToCSV"):
      in_df = op.input
      out_df = op.outputs[0]
  
      if in_df.ndim == 2 and in_df.chunk_shape[1] > 1:
          # make sure only 1 chunk on the column axis
          in_df = yield from recursive_tile(
              in_df.rechunk({1: in_df.shape[1]})
          )
  
      one_file = op.one_file
  
      out_chunks = [], []
      for chunk in in_df.chunks:
          chunk_op = op.copy().reset_key()
          if not one_file:
              index_value = parse_index(
                  chunk.index_value.to_pandas()[:0], chunk
              )
              if chunk.ndim == 2:
                  out_chunk = chunk_op.new_chunk(
                      [chunk],
                      shape=(0, 0),
                      index_value=index_value,
                      columns_value=out_df.columns_value,
                      dtypes=out_df.dtypes,
                      index=chunk.index,
                  )
              else:
                  out_chunk = chunk_op.new_chunk(
                      [chunk],
                      shape=(0,),
                      index_value=index_value,
                      dtype=out_df.dtype,
                      index=chunk.index,
                  )
              out_chunks[0].append(out_chunk)
          else:
              chunk_op._output_stat = True
              chunk_op.stage = OperandStage.map
              chunk_op.output_types = [OutputType.scalar] * 2
              # bytes of csv
              kws = [
                  {
                      "shape": (),
                      "dtype": np.dtype(np.str_),
                      "index": chunk.index,
                      "order": TensorOrder.C_ORDER,
                      "output_type": OutputType.scalar,
                      "type": "csv",
                  },
                  {
                      "shape": (),
                      "dtype": np.dtype(np.intp),
                      "index": chunk.index,
                      "order": TensorOrder.C_ORDER,
                      "output_type": OutputType.scalar,
                      "type": "stat",
                  },
              ]
              chunks = chunk_op.new_chunks(
                  [chunk], kws=kws, output_limit=len(kws)
              )
              out_chunks[0].append(chunks[0])
              out_chunks[1].append(chunks[1])
  
      if not one_file:
          out_chunks = out_chunks[0]
      else:
          stat_chunk = DataFrameToCSVStat(
              path=op.path,
              dtype=np.dtype(np.int64),
              storage_options=op.storage_options,
          ).new_chunk(
              out_chunks[1],
              shape=(len(out_chunks[0]),),
              order=TensorOrder.C_ORDER,
          )
          new_out_chunks = []
          for c in out_chunks[0]:
              op = DataFrameToCSV(
                  stage=OperandStage.agg,
                  path=op.path,
                  storage_options=op.storage_options,
                  output_types=op.output_types,
              )
              if out_df.ndim == 2:
                  out_chunk = op.new_chunk(
                      [c, stat_chunk],
                      shape=(0, 0),
                      dtypes=out_df.dtypes,
                      index_value=out_df.index_value,
                      columns_value=out_df.columns_value,
                      index=c.index,
                  )
              else:
                  out_chunk = op.new_chunk(
                      [c, stat_chunk],
                      shape=(0,),
                      dtype=out_df.dtype,
                      index_value=out_df.index_value,
                      index=c.index,
                  )
              new_out_chunks.append(out_chunk)
          out_chunks = new_out_chunks
  
      new_op = op.copy()
      params = out_df.params.copy()
      if out_df.ndim == 2:
          params.update(
              dict(
                  chunks=out_chunks,
                  nsplits=((0,) * in_df.chunk_shape[0], (0,)),
              )
          )
      else:
          params.update(
              dict(
                  chunks=out_chunks,
                  nsplits=((0,) * in_df.chunk_shape[0],),
              )
          )
      return new_op.new_tileables([in_df], **params)
  
imports: from io import StringIO
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import OutputType, recursive_tile
  from ...core.operand import OperandStage
  from ...lib.filesystem import open_file
  from ...serialization.serializables import KeyField, AnyField, StringField, ListField, BoolField, Int32Field, Int64Field, DictField
  from ...tensor.core import TensorOrder
  from ...tensor.operands import TensorOperand, TensorOperandMixin
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index
label_desc: Use image node_id 27 for calling the DataFrameToCSV obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op