class_docstr: A list-like object with feature names. It allows
  feature names for unknown features to be generated using
  a provided template, and to avoid making copies of large objects
  in get_feature_names.
class_docstr_tok: ["A","list-like","object","with","feature","names",".","It","allows","feature","names","for","unknown","features","to","be","generated","using","a","provided","template",",","and","to","avoid","making","copies","of","large","objects","in","get_feature_names","."]
code: def handle_filter(
      self,
      feature_filter,
      feature_re,  # type: Pattern[str]
      x=None,  # type: Any
  ):
      # type: (...) -> Tuple[FeatureNames, Union[List[int], None]]
      if feature_re is not None and feature_filter:
          raise ValueError("pass either feature_filter or feature_re")
      if feature_re is not None:
          if x is not None:
              feature_filter = lambda name, _: re.search(
                  feature_re, name
              )
          else:
              feature_filter = lambda name: re.search(feature_re, name)
  
      if feature_filter is not None:
          return self.filtered(feature_filter, x)
      else:
          return self, None
  
file_path: eli5/eli5/_feature_names.py
imports: import re
  import six
  from typing import Any, Iterable, Iterator, Tuple, Sized, List, Optional, Dict, Union, Callable, Pattern
  import numpy
  import scipy.sparse
label_desc: Use image node_id 10 for calling the FeatureNames obj's underlying member method code with example usage: obj.handle_filter(feature_filter, feature_re, x) and returns: self, self,  None