class_docstr: Class for recovering a mapping used by FeatureHasher.
docstr: Update all computed attributes. It is only needed if you need to access
  computed attributes after :meth:`patrial_fit` was called.
code: def recalculate_attributes(self, force=False):
      # type: (bool) -> None
      """
      Update all computed attributes. It is only needed if you need to access
      computed attributes after :meth:`patrial_fit` was called.
      """
      if not self._attributes_dirty and not force:
          return
      terms = [term for term, _ in self._term_counts.most_common()]
      if six.PY2:
          terms = np.array(terms, dtype=np.object)
      else:
          terms = np.array(terms)
      if len(terms):
          indices, signs = _get_indices_and_signs(self.hasher, terms)
      else:
          indices, signs = np.array([]), np.array([])
      self.terms_ = terms  # type: np.ndarray
      self.term_columns_ = indices
      self.term_signs_ = signs
      self.collisions_ = _get_collisions(indices)
      self.column_signs_ = self._get_column_signs()
      self._attributes_dirty = False
  
imports: from __future__ import absolute_import
  from collections import defaultdict, Counter
  from itertools import chain
  from typing import List, Iterable, Any, Dict, Tuple, Union
  import numpy
  import six
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_extraction.text import HashingVectorizer, FeatureHasher
  from sklearn.pipeline import FeatureUnion
  from eli5._feature_names import FeatureNames
label_desc: Use image node_id 5 for calling the FeatureUnhasher obj's underlying member method code with example usage: obj.recalculate_attributes(force) without return types