code: def _tile_scalar(cls, op):
      tileable = op.rhs if pd.api.types.is_scalar(op.lhs) else op.lhs
      df = op.outputs[0]
      out_chunks = []
      lazy_chunk_meta = is_chunk_meta_lazy(tileable.chunks[0])
      for chunk in tileable.chunks:
          out_op = op.copy().reset_key()
          if chunk.ndim == 2:
              if lazy_chunk_meta:
                  out_chunk = out_op.new_chunk(
                      [chunk],
                      shape=chunk.shape,
                      index=chunk.index,
                  )
                  out_chunk._set_tileable_meta(
                      tileable_key=df.key,
                      nsplits=tileable.nsplits,
                      index_value=df.index_value,
                      columns_value=df.columns_value,
                      dtypes=df.dtypes,
                  )
              else:
                  out_chunk = out_op.new_chunk(
                      [chunk],
                      shape=chunk.shape,
                      index=chunk.index,
                      dtypes=chunk.dtypes,
                      index_value=chunk.index_value,
                      columns_value=getattr(chunk, "columns_value"),
                  )
          else:
              if lazy_chunk_meta:
                  out_chunk = out_op.new_chunk(
                      [chunk],
                      shape=chunk.shape,
                      index=chunk.index,
                      dtype=chunk.dtype,
                      name=getattr(chunk, "name"),
                  )
                  out_chunk._set_tileable_meta(
                      tileable_key=df.key,
                      nsplits=tileable.nsplits,
                      index_value=df.index_value,
                  )
              else:
                  out_chunk = out_op.new_chunk(
                      [chunk],
                      shape=chunk.shape,
                      index=chunk.index,
                      dtype=chunk.dtype,
                      index_value=chunk.index_value,
                      name=getattr(chunk, "name"),
                  )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      params = df.params.copy()
      params["chunks"] = out_chunks
      params["nsplits"] = tileable.nsplits
      return new_op.new_tileables(op.inputs, kws=[params])
  
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 5 for calling the DataFrameBinOpMixin obj's underlying member method code with example usage: obj._tile_scalar(cls, op) and returns: new_op