class_docstr: Class for recovering a mapping used by FeatureHasher.
code: def __init__(self, hasher, unkn_template="FEATURE[%d]"):
      # type: (FeatureHasher, str) -> None
      if hasher.input_type != "string":
          raise ValueError(
              "FeatureUnhasher only supports hashers with "
              "input_type 'string', got %r." % hasher.input_type
          )
      self.hasher = hasher
      self.n_features = self.hasher.n_features  # type: int
      self.unkn_template = unkn_template
      self._attributes_dirty = True
      self._term_counts = Counter()
  
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 1 to create a new FeatureUnhasher object from inherited base classes: BaseEstimator with example: obj = FeatureUnhasher(hasher, unkn_template)