code: def _tile_with_tensor(cls, op):
      out = op.outputs[0]
      axis = op.axis
      if axis is None:
          axis = 0
  
      rhs_is_tensor = isinstance(op.rhs, TENSOR_TYPE)
      tensor, other = (
          (op.rhs, op.lhs) if rhs_is_tensor else (op.lhs, op.rhs)
      )
      if tensor.shape == other.shape:
          tensor = yield from recursive_tile(
              tensor.rechunk(other.nsplits)
          )
      else:
          # shape differs only when dataframe add 1-d tensor, we need rechunk on columns axis.
          if axis in ["columns", 1] and other.ndim == 1:
              # force axis == 0 if it's Series other than DataFrame
              axis = 0
          rechunk_size = (
              other.nsplits[1]
              if axis == "columns" or axis == 1
              else other.nsplits[0]
          )
          if tensor.ndim > 0:
              tensor = yield from recursive_tile(
                  tensor.rechunk((rechunk_size,))
              )
  
      out_chunks = []
      for out_index in itertools.product(
          *(map(range, other.chunk_shape))
      ):
          tensor_chunk = tensor.cix[out_index[: tensor.ndim]]
          other_chunk = other.cix[out_index]
          out_op = op.copy().reset_key()
          inputs = (
              [other_chunk, tensor_chunk]
              if rhs_is_tensor
              else [tensor_chunk, other_chunk]
          )
          if isinstance(other_chunk, DATAFRAME_CHUNK_TYPE):
              cum_splits = [0] + np.cumsum(other.nsplits[1]).tolist()
              start = cum_splits[out_index[1]]
              end = cum_splits[out_index[1] + 1]
              chunk_dtypes = out.dtypes.iloc[start:end]
              out_chunk = out_op.new_chunk(
                  inputs,
                  shape=other_chunk.shape,
                  index=other_chunk.index,
                  dtypes=chunk_dtypes,
                  index_value=other_chunk.index_value,
                  columns_value=other_chunk.columns_value,
              )
          else:
              out_chunk = out_op.new_chunk(
                  inputs,
                  shape=other_chunk.shape,
                  index=other_chunk.index,
                  dtype=out.dtype,
                  index_value=other_chunk.index_value,
                  name=other_chunk.name,
              )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      if isinstance(other, SERIES_TYPE):
          return new_op.new_seriess(
              op.inputs,
              other.shape,
              nsplits=other.nsplits,
              dtype=out.dtype,
              name=other.name,
              index_value=other.index_value,
              chunks=out_chunks,
          )
      else:
          return new_op.new_dataframes(
              op.inputs,
              other.shape,
              nsplits=other.nsplits,
              dtypes=out.dtypes,
              index_value=other.index_value,
              columns_value=other.columns_value,
              chunks=out_chunks,
          )
  
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 6 for calling the DataFrameBinOpMixin obj's underlying member method code with example usage: obj._tile_with_tensor(cls, op) and returns: new_op, new_op