class_docstr: Fake vectorizer which converts document just to a vector of ones 
code: def fit(self, X, y=None):
      self.text_ = X[0]
      if not isinstance(self.text_, TokenizedText):
          self.text_ = TokenizedText(
              self.text_, token_pattern=self.token_pattern
          )
      return self
  
file_path: eli5/eli5/lime/_vectorizer.py
imports: from __future__ import absolute_import
  from typing import Tuple, Callable, Dict, Optional, List
  import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from eli5.base import DocWeightedSpans, FeatureWeights
  from eli5.sklearn.text import _get_feature_weights_dict
  from .textutils import TokenizedText
label_desc: Use image node_id 2 for calling the SingleDocumentVectorizer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self