code: def _tile_both_dataframes(cls, op):
      # if both of the inputs are DataFrames, axis is just ignored
      left, right = op.lhs, op.rhs
      df = op.outputs[0]
  
      (
          nsplits,
          out_shapes,
          left_chunks,
          right_chunks,
      ) = align_dataframe_dataframe(left, right)
      out_chunk_indexes = itertools.product(
          *(range(s) for s in out_shapes[0])
      )
  
      out_chunks = []
      for idx, left_chunk, right_chunk in zip(
          out_chunk_indexes, left_chunks, right_chunks
      ):
          out_chunk = (
              op.copy()
              .reset_key()
              .new_chunk(
                  [left_chunk, right_chunk],
                  shape=(nsplits[0][idx[0]], nsplits[1][idx[1]]),
                  index=idx,
              )
          )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      return new_op.new_dataframes(
          op.inputs,
          df.shape,
          nsplits=tuple(tuple(ns) for ns in nsplits),
          chunks=out_chunks,
          dtypes=df.dtypes,
          index_value=df.index_value,
          columns_value=df.columns_value,
      )
  
imports: import itertools
  import copy
  from functools import reduce
  import numpy
  import pandas
  from ...core import ENTITY_TYPE, CHUNK_TYPE, recursive_tile
  from ...serialization.serializables import AnyField
  from ...tensor.core import TENSOR_TYPE, TENSOR_CHUNK_TYPE, ChunkData, Chunk
  from ...utils import classproperty, get_dtype
  from ..align import align_series_series, align_dataframe_series, align_dataframe_dataframe
  from ..core import DATAFRAME_TYPE, SERIES_TYPE, DATAFRAME_CHUNK_TYPE, SERIES_CHUNK_TYPE, is_chunk_meta_lazy
  from ..operands import DataFrameOperandMixin, DataFrameOperand
  from ..ufunc.tensor import TensorUfuncMixin
  from ..utils import parse_index, infer_dtypes, infer_dtype, infer_index_value, build_empty_df
label_desc: Use image node_id 1 for calling the DataFrameBinOpMixin obj's underlying member method code with example usage: obj._tile_both_dataframes(cls, op) and returns: new_op