class_docstr: Fake vectorizer which converts document just to a vector of ones 
code: def get_doc_weighted_spans(
      self,
      doc,  # type: str
      feature_weights,  # type: FeatureWeights
      feature_fn,  # type: Callable[[str], str]
  ):
      # type: (...) -> Tuple[Dict[Tuple[str, int], float], DocWeightedSpans]
      feature_weights_dict = _get_feature_weights_dict(
          feature_weights, feature_fn
      )
      spans = []
      found_features = {}
      for idx, (span, feature) in enumerate(
          self.text_.spans_and_tokens
      ):
          featname = self._featname(idx, feature)
          if featname not in feature_weights_dict:
              continue
          weight, key = feature_weights_dict[featname]
          spans.append((feature, [span], weight))
          found_features[key] = weight
  
      doc_weighted_spans = DocWeightedSpans(
          document=doc,
          spans=spans,
          preserve_density=False,
      )
      return found_features, doc_weighted_spans
  
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 4 for calling the SingleDocumentVectorizer obj's underlying member method code with example usage: obj.get_doc_weighted_spans(doc, feature_weights, feature_fn) and returns: found_features,  doc_weighted_spans