code: def tile(cls, op: "DataFrameDrop"):
      inp = op.inputs[0]
      out = op.outputs[0]
      if len(op.inputs) > 1:
          rechunked = yield from recursive_tile(
              op.index.rechunk({0: (op.index.shape[0],)})
          )
          index_chunk = rechunked.chunks[0]
      else:
          index_chunk = op.index
  
      col_to_args = OrderedDict()
      chunks = []
      for c in inp.chunks:
          params = c.params.copy()
          if isinstance(inp, DATAFRAME_TYPE):
              new_dtypes, new_col_id = col_to_args.get(
                  c.index[1], (None, None)
              )
  
              if new_dtypes is None:
                  new_col_id = len(col_to_args)
                  new_dtypes = op._filter_dtypes(
                      c.dtypes, ignore_errors=True
                  )
                  if len(new_dtypes) == 0:
                      continue
                  col_to_args[c.index[1]] = (new_dtypes, new_col_id)
  
              params.update(
                  dict(
                      dtypes=new_dtypes,
                      index=(c.index[0], new_col_id),
                      index_value=c.index_value,
                      columns_value=parse_index(
                          new_dtypes.index, store_data=True
                      ),
                  )
              )
              if op.index is not None:
                  params.update(
                      dict(
                          shape=(np.nan, len(new_dtypes)),
                          index_value=parse_index(
                              None, (c.key, c.index_value.key)
                          ),
                      )
                  )
              else:
                  params["shape"] = (c.shape[0], len(new_dtypes))
          elif op.index is not None:
              params.update(
                  dict(
                      shape=(np.nan,),
                      index_value=parse_index(
                          None, (c.key, c.index_value.key)
                      ),
                  )
              )
  
          chunk_inputs = [c]
          if isinstance(index_chunk, Chunk):
              chunk_inputs.append(index_chunk)
  
          new_op = op.copy().reset_key()
          new_op._index = index_chunk
          chunks.append(new_op.new_chunk(chunk_inputs, **params))
  
      new_op = op.copy().reset_key()
      params = out.params.copy()
      if op.index is not None:
          nsplits_list = [(np.nan,) * inp.chunk_shape[0]]
      else:
          nsplits_list = [inp.nsplits[0]]
      if isinstance(inp, DATAFRAME_TYPE):
          nsplits_list.append(
              tuple(len(dt) for dt, _ in col_to_args.values())
          )
      params.update(dict(chunks=chunks, nsplits=tuple(nsplits_list)))
      return new_op.new_tileables(op.inputs, **params)
  
imports: import warnings
  from collections import OrderedDict
  import numpy
  from ...None import opcodes
  from ...core import Entity, Chunk, CHUNK_TYPE, OutputType, recursive_tile
  from ...serialization.serializables import AnyField, StringField
  from ..core import IndexValue, DATAFRAME_TYPE, SERIES_TYPE, INDEX_CHUNK_TYPE
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, validate_axis
label_desc: Use image node_id 9 for calling the DataFrameDrop obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op