class_docstr: Contains a filter function which works on a dataframe and a label describing the filter.
  
  Parameters
  ----------
  filter_functions : List[Callable], default: None
      List of functions that receive a DataFrame and return a filter on it. If None, no filter is applied
  label : str, default = ''
      name of the filter
docstr: Run the filter on given dataframe. Return rows in data frame satisfying the filter properties.
docstr_tok: ["Run","the","filter","on","given","dataframe",".","Return","rows","in","data","frame","satisfying","the","filter","properties","."]
code: def filter(
      self,
      dataframe: pd.DataFrame,
      label_col: Optional[pd.Series] = None,
  ) -> Union[Tuple[pd.DataFrame, pd.Series], pd.DataFrame]:
      """Run the filter on given dataframe. Return rows in data frame satisfying the filter properties."""
      if label_col is not None:
          dataframe["temp_label_col"] = label_col
      for func in self.filter_functions:
          dataframe = dataframe.loc[func(dataframe)]
  
      if label_col is not None:
          return (
              dataframe.drop(columns=["temp_label_col"]),
              dataframe["temp_label_col"],
          )
      else:
          return dataframe
  
imports: from collections import defaultdict
  from copy import deepcopy
  from typing import Callable, List, Optional, Tuple, Union
  import numpy
  import pandas
  from sklearn.tree import _tree
  from deepchecks.tabular.dataset import Dataset
  from deepchecks.utils.strings import format_number
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 2 for calling the DeepchecksFilter obj's underlying member method code with example usage: obj.filter(dataframe, label_col) and returns: dataframe,  dataframe, dataframe