code: def _tile_dataframe(cls, op):
      in_df = op.inputs[0]
      out = op.outputs[0]
      cum_nsplits = np.cumsum((0,) + in_df.nsplits[1])
      out_chunks = []
  
      if op.dtype_values == "category":
          # all columns need unique values
          for c in in_df.chunks:
              chunk_op = op.copy().reset_key()
              params = c.params.copy()
              dtypes = out.dtypes[
                  cum_nsplits[c.index[1]] : cum_nsplits[c.index[1] + 1]
              ]
              params["dtypes"] = dtypes
              chunk_op._category_cols = list(
                  c.columns_value.to_pandas()
              )
              unique_chunks = []
              for col in c.columns_value.to_pandas():
                  unique = yield from recursive_tile(
                      sort(in_df[col].unique())
                  )
                  unique_chunks.append(unique.chunks[0])
              new_chunk = chunk_op.new_chunk(
                  [c] + unique_chunks, **params
              )
              out_chunks.append(new_chunk)
      elif (
          isinstance(op.dtype_values, dict)
          and "category" in op.dtype_values.values()
      ):
          # some columns' types are category
          category_cols = [
              c
              for c, v in op.dtype_values.items()
              if isinstance(v, str) and v == "category"
          ]
          unique_chunks = dict()
          for col in category_cols:
              unique = yield from recursive_tile(
                  sort(in_df[col].unique())
              )
              unique_chunks[col] = unique.chunks[0]
          for c in in_df.chunks:
              chunk_op = op.copy().reset_key()
              params = c.params.copy()
              dtypes = out.dtypes[
                  cum_nsplits[c.index[1]] : cum_nsplits[c.index[1] + 1]
              ]
              params["dtypes"] = dtypes
              chunk_category_cols = []
              chunk_unique_chunks = []
              for col in c.columns_value.to_pandas():
                  if col in category_cols:
                      chunk_category_cols.append(col)
                      chunk_unique_chunks.append(unique_chunks[col])
              chunk_op._category_cols = chunk_category_cols
              new_chunk = chunk_op.new_chunk(
                  [c] + chunk_unique_chunks, **params
              )
              out_chunks.append(new_chunk)
      else:
          for c in in_df.chunks:
              chunk_op = op.copy().reset_key()
              params = c.params.copy()
              dtypes = out.dtypes[
                  cum_nsplits[c.index[1]] : cum_nsplits[c.index[1] + 1]
              ]
              params["dtypes"] = dtypes
              new_chunk = chunk_op.new_chunk([c], **params)
              out_chunks.append(new_chunk)
  
      new_op = op.copy()
      return new_op.new_dataframes(
          op.inputs,
          nsplits=in_df.nsplits,
          chunks=out_chunks,
          **out.params.copy(),
      )
  
imports: import numpy
  import pandas
  from pandas.api.types import CategoricalDtype
  from ...None import opcodes
  from ...core import recursive_tile
  from ...serialization.serializables import AnyField, StringField, ListField
  from ...tensor.base import sort
  from ...utils import pd_release_version
  from ..core import DATAFRAME_TYPE, SERIES_TYPE
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import build_empty_df, build_empty_series, parse_index
label_desc: Use image node_id 7 for calling the DataFrameAstype obj's underlying member method code with example usage: obj._tile_dataframe(cls, op) and returns: new_op