code: def tile(cls, op: "DataFrameCartesianChunk"):
      left = op.left
      right = op.right
      out = op.outputs[0]
      out_type = op.output_types[0]
  
      if left.ndim == 2 and left.chunk_shape[1] > 1:
          if has_unknown_shape(left):
              yield
          # if left is a DataFrame, make sure 1 chunk on axis columns
          left = yield from recursive_tile(
              left.rechunk({1: left.shape[1]})
          )
      if right.ndim == 2 and right.chunk_shape[1] > 1:
          if has_unknown_shape(right):
              yield
          # if right is a DataFrame, make sure 1 chunk on axis columns
          right = yield from recursive_tile(
              right.rechunk({1: right.shape[1]})
          )
  
      out_chunks = []
      if out_type == OutputType.dataframe:
          nsplits = [[], [out.shape[1]]]
      elif out_type == OutputType.series:
          nsplits = [[]]
      else:
          # DataFrameOrSeries
          nsplits = None
      i = 0
      for left_chunk in left.chunks:
          for right_chunk in right.chunks:
              chunk_op = op.copy().reset_key()
              chunk_op.tileable_op_key = op.key
              if out_type == OutputType.df_or_series:
                  out_chunks.append(
                      chunk_op.new_chunk(
                          [left_chunk, right_chunk],
                          index=(i, 0),
                          collapse_axis=1,
                      )
                  )
              elif out_type == OutputType.dataframe:
                  shape = (np.nan, out.shape[1])
                  index_value = parse_index(
                      out.index_value.to_pandas(),
                      left_chunk,
                      right_chunk,
                      op.func,
                      op.args,
                      op.kwargs,
                  )
                  out_chunk = chunk_op.new_chunk(
                      [left_chunk, right_chunk],
                      shape=shape,
                      index_value=index_value,
                      columns_value=out.columns_value,
                      dtypes=out.dtypes,
                      index=(i, 0),
                  )
                  out_chunks.append(out_chunk)
                  nsplits[0].append(out_chunk.shape[0])
              else:
                  shape = (np.nan,)
                  index_value = parse_index(
                      out.index_value.to_pandas(),
                      left_chunk,
                      right_chunk,
                      op.func,
                      op.args,
                      op.kwargs,
                  )
                  out_chunk = chunk_op.new_chunk(
                      [left_chunk, right_chunk],
                      shape=shape,
                      index_value=index_value,
                      dtype=out.dtype,
                      name=out.name,
                      index=(i,),
                  )
                  out_chunks.append(out_chunk)
                  nsplits[0].append(out_chunk.shape[0])
              i += 1
  
      params = out.params
      params["nsplits"] = (
          tuple(tuple(ns) for ns in nsplits) if nsplits else nsplits
      )
      params["chunks"] = out_chunks
      new_op = op.copy()
      return new_op.new_tileables(op.inputs, kws=[params])
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...core import recursive_tile
  from ...core.custom_log import redirect_custom_log
  from ...serialization.serializables import KeyField, FunctionField, TupleField, DictField
  from ...utils import has_unknown_shape, enter_current_session, quiet_stdio
  from ..operands import DataFrameOperand, DataFrameOperandMixin, OutputType
  from ..utils import build_df, build_series, build_empty_df, parse_index, validate_output_types
label_desc: Use image node_id 10 for calling the DataFrameCartesianChunk obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op