code: def tile_with_mask(cls, op: "DataFrameIndex"):
      in_df = op.inputs[0]
      out_df = op.outputs[0]
  
      out_chunks = []
  
      if isinstance(
          op.mask, (SERIES_TYPE, DATAFRAME_TYPE, TENSOR_TYPE)
      ):
          mask = op.inputs[1]
  
          if (
              hasattr(mask, "index_value")
              and mask.ndim == 1
              and op.identical_index
          ):
              if has_unknown_shape(in_df, mask):
                  yield
              nsplits = (
                  (np.nan,) * in_df.chunk_shape[0],
                  in_df.nsplits[1],
              )
              out_shape = in_df.chunk_shape
              df_chunks = in_df.chunks
              aligned_mask = yield from recursive_tile(
                  mask.rechunk(in_df.nsplits[: mask.ndim])
              )
              mask_chunks = aligned_mask.chunks
          elif isinstance(mask, SERIES_TYPE):
              (
                  nsplits,
                  out_shape,
                  df_chunks,
                  mask_chunks,
              ) = align_dataframe_series(in_df, mask, axis="index")
          elif isinstance(mask, DATAFRAME_TYPE):
              (
                  nsplits,
                  out_shapes,
                  df_chunks,
                  mask_chunks,
              ) = align_dataframe_dataframe(in_df, mask)
              out_shape = out_shapes[0]
          else:
              # tensor
              nsplits = in_df.nsplits
              mask = yield from recursive_tile(
                  mask.rechunk(nsplits[: mask.ndim])
              )
              out_shape = in_df.chunk_shape
              df_chunks = in_df.chunks
              mask_chunks = mask.chunks
          out_chunk_indexes = itertools.product(
              *(range(s) for s in out_shape)
          )
  
          out_chunks = []
          for i, idx, df_chunk in zip(
              itertools.count(), out_chunk_indexes, df_chunks
          ):
              if op.mask.ndim == 1:
                  mask_chunk = mask_chunks[df_chunk.index[0]]
              else:
                  mask_chunk = mask_chunks[i]
              out_chunk = (
                  op.copy()
                  .reset_key()
                  .new_chunk(
                      [df_chunk, mask_chunk],
                      index=idx,
                      shape=(np.nan, df_chunk.shape[1]),
                  )
              )
              out_chunk._set_tileable_meta(
                  tileable_key=out_df.key,
                  nsplits=nsplits,
                  index_value=out_df.index_value,
                  columns_value=out_df.columns_value,
                  dtypes=out_df.dtypes,
              )
              out_chunks.append(out_chunk)
      else:
          if has_unknown_shape(in_df):
              yield
          nsplits_acc = np.cumsum((0,) + in_df.nsplits[0])
          for idx in range(in_df.chunk_shape[0]):
              for idxj in range(in_df.chunk_shape[1]):
                  in_chunk = in_df.cix[idx, idxj]
                  chunk_op = op.copy().reset_key()
                  chunk_op.mask = op.mask.iloc[
                      nsplits_acc[idx] : nsplits_acc[idx + 1]
                  ]
                  out_chunk = chunk_op.new_chunk(
                      [in_chunk],
                      index=in_chunk.index,
                      shape=(np.nan, in_chunk.shape[1]),
                      dtypes=in_chunk.dtypes,
                      index_value=in_df.index_value,
                      columns_value=in_chunk.columns_value,
                  )
                  out_chunks.append(out_chunk)
  
      nsplits_on_columns = tuple(
          c.shape[1] for c in out_chunks if c.index[0] == 0
      )
      row_chunk_num = len(
          [c.shape[0] for c in out_chunks if c.index[1] == 0]
      )
      nsplits = ((np.nan,) * row_chunk_num, nsplits_on_columns)
  
      new_op = op.copy()
      return new_op.new_dataframes(
          op.inputs,
          shape=out_df.shape,
          dtypes=out_df.dtypes,
          index_value=out_df.index_value,
          columns_value=out_df.columns_value,
          chunks=out_chunks,
          nsplits=nsplits,
      )
  
imports: import itertools
  from numbers import Integral
  import pandas
  import numpy
  from ...None import opcodes
  from ...config import options
  from ...core import ENTITY_TYPE, OutputType, recursive_tile
  from ...serialization.serializables import AnyField, Int32Field, BoolField
  from ...tensor.core import TENSOR_TYPE, TENSOR_CHUNK_TYPE
  from ...tensor.datasource import tensor
  from ...utils import has_unknown_shape
  from ..align import align_dataframe_series, align_dataframe_dataframe
  from ..core import SERIES_TYPE, SERIES_CHUNK_TYPE, DATAFRAME_TYPE, DATAFRAME_CHUNK_TYPE, is_chunk_meta_lazy
  from ..merge import DataFrameConcat
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, in_range_index
  from .utils import calc_columns_index
label_desc: Use image node_id 5 for calling the DataFrameIndex obj's underlying member method code with example usage: obj.tile_with_mask(cls, op) and returns: new_op