code: def tile(cls, op: "TransformOperand"):
      in_df = op.inputs[0]
      out_df = op.outputs[0]
      axis = op.axis
  
      if isinstance(in_df, DATAFRAME_TYPE):
          if in_df.chunk_shape[axis] > 1:
              chunk_size = (
                  in_df.shape[axis],
                  max(
                      1, options.chunk_store_limit // in_df.shape[axis]
                  ),
              )
              if axis == 1:
                  chunk_size = chunk_size[::-1]
              in_df = yield from recursive_tile(
                  in_df.rechunk(chunk_size)
              )
      elif isinstance(op.func, str) or (
          isinstance(op.func, list)
          and any(isinstance(e, str) for e in op.func)
      ):
          # builtin cols handles whole columns, thus merge is needed
          if in_df.chunk_shape[0] > 1:
              in_df = yield from recursive_tile(
                  in_df.rechunk((in_df.shape[axis],))
              )
  
      chunks = []
      axis_index_map = dict()
      col_sizes = []
      for c in in_df.chunks:
          new_op = op.copy().reset_key()
          new_op.tileable_op_key = op.key
          params = c.params.copy()
  
          if out_df.ndim == 2:
              if out_df.dtypes is None:
                  new_dtypes = None
                  new_shape = [c.shape[0], np.nan]
                  new_index = c.index
                  new_columns_value = None
                  if c.index[0] == 0:
                      col_sizes.append(np.nan)
              elif isinstance(c, DATAFRAME_CHUNK_TYPE):
                  columns = c.columns_value.to_pandas()
                  new_dtypes = filter_dtypes_by_index(
                      out_df.dtypes, columns
                  )
  
                  if len(new_dtypes) == 0:
                      continue
                  if c.index[0] == 0:
                      col_sizes.append(len(new_dtypes))
  
                  new_index = list(c.index)
                  try:
                      new_index[1 - op.axis] = axis_index_map[
                          c.index[1 - op.axis]
                      ]
                  except KeyError:
                      new_index[1 - op.axis] = axis_index_map[
                          c.index[1 - op.axis]
                      ] = len(axis_index_map)
  
                  if isinstance(op.func, dict):
                      new_op._func = dict(
                          (k, v)
                          for k, v in op.func.items()
                          if k in new_dtypes
                      )
  
                  new_shape = list(c.shape)
                  new_shape[1] = len(new_dtypes)
  
                  if op.call_agg:
                      new_shape[op.axis] = np.nan
                      params["index_value"] = parse_index(
                          None, c.key, c.index_value.key
                      )
                  new_columns_value = parse_index(new_dtypes.index)
              else:
                  new_dtypes = out_df.dtypes
                  new_index = c.index + (0,)
                  new_shape = [c.shape[0], len(new_dtypes)]
                  if op.call_agg:
                      new_shape[0] = np.nan
                  if c.index[0] == 0:
                      col_sizes.append(len(new_dtypes))
                  new_columns_value = out_df.columns_value
              params.update(
                  dict(
                      dtypes=new_dtypes,
                      shape=tuple(new_shape),
                      index=tuple(new_index),
                      columns_value=new_columns_value,
                  )
              )
          else:
              params["dtype"] = out_df.dtype
              if isinstance(in_df, DATAFRAME_TYPE):
                  params.pop("columns_value", None)
                  params["index_value"] = out_df.index_value
                  params["shape"] = (c.shape[1 - op.axis],)
                  params["index"] = (c.index[1 - op.axis],)
          chunks.append(new_op.new_chunk([c], **params))
  
      if out_df.ndim == 2:
          new_nsplits = [in_df.nsplits[0], tuple(col_sizes)]
          if op.call_agg:
              new_nsplits[op.axis] = (np.nan,)
      elif op.call_agg:
          if isinstance(in_df, DATAFRAME_TYPE):
              new_nsplits = (in_df.nsplits[1],)
          else:
              new_nsplits = ((np.nan,),)
      else:
          new_nsplits = in_df.nsplits
  
      new_op = op.copy()
      kw = out_df.params.copy()
      kw.update(dict(chunks=chunks, nsplits=tuple(new_nsplits)))
      return new_op.new_tileables(op.inputs, **kw)
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...config import options
  from ...core import OutputType, recursive_tile
  from ...core.custom_log import redirect_custom_log
  from ...serialization.serializables import AnyField, BoolField, TupleField, DictField
  from ...utils import enter_current_session, quiet_stdio, pd_release_version
  from ..core import DATAFRAME_CHUNK_TYPE, DATAFRAME_TYPE
  from ..operands import DataFrameOperandMixin, DataFrameOperand
  from ..utils import build_df, build_series, validate_axis, parse_index, filter_dtypes_by_index, make_dtypes
label_desc: Use image node_id 9 for calling the TransformOperand obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op