class_docstr: Fake vectorizer which converts document just to a vector of ones 
code: def __init__(self, token_pattern):
      # type: (str) -> None
      self.token_pattern = token_pattern
  
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 1 to create a new SingleDocumentVectorizer object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = SingleDocumentVectorizer(token_pattern)