code: def __call__(self, df):
      if self.col_names is not None:
          # if col_names is a list, return a DataFrame, else return a Series
          col_names = self.col_names
          if not isinstance(col_names, list):
              col_names = [col_names]
              is_list = False
          else:
              is_list = True
  
          dtypes_list = df._get_dtypes_by_columns(col_names)
          if is_list or len(dtypes_list) > 1:
              if len(col_names) != len(dtypes_list):
                  col_names = df._get_columns_by_columns(col_names)
              columns = parse_index(
                  pd.Index(col_names), store_data=True
              )
              return self.new_dataframe(
                  [df],
                  shape=(df.shape[0], len(col_names)),
                  dtypes=pd.Series(
                      dtypes_list, index=col_names, dtype=np.dtype("O")
                  ),
                  index_value=df.index_value,
                  columns_value=columns,
              )
          else:
              dtype = dtypes_list[0]
              return self.new_series(
                  [df],
                  shape=(df.shape[0],),
                  dtype=dtype,
                  index_value=df.index_value,
                  name=self.col_names,
              )
      else:
          if isinstance(
              self.mask, (SERIES_TYPE, DATAFRAME_TYPE, TENSOR_TYPE)
          ):
              index_value = parse_index(
                  pd.Index(
                      [],
                      dtype=df.index_value.to_pandas().dtype,
                      name=df.index_value.name,
                  ),
                  df,
                  self.mask,
              )
              return self.new_dataframe(
                  [df, self.mask],
                  shape=(np.nan, df.shape[1]),
                  dtypes=df.dtypes,
                  index_value=index_value,
                  columns_value=df.columns_value,
              )
          else:
              index_value = parse_index(
                  pd.Index(
                      [],
                      dtype=df.index_value.to_pandas().dtype,
                      name=df.index_value.name,
                  ),
                  df,
                  self.mask,
              )
              return self.new_dataframe(
                  [df],
                  shape=(np.nan, df.shape[1]),
                  dtypes=df.dtypes,
                  index_value=index_value,
                  columns_value=df.columns_value,
              )
  
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 3 for calling the DataFrameIndex obj's underlying member method code with example usage: obj.__call__(df) and returns: self, self, self, self