code: def vectorization(self, x):
      v = self.word_vectors(x)
      if v.shape[0] == 0:
          return np.zeros(self._L.shape[1])
      s = np.dot(v, self._L)
      return s.max(axis=0)
  
imports: from gsitk.datasets.datasets import DatasetManager
  from nltk.corpus import opinion_lexicon
  from collections import Counter
  from gensim.models import Word2Vec
  from numpy import array, dot
  from gensim import matutils
  import collections.abc
  import functools
  from gsitk.features.word2vec import Word2VecFeatures
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.feature_extraction import DictVectorizer
  from sklearn.pipeline import Pipeline
  import numpy
  import nltk
  from nltk.corpus import opinion_lexicon
  from collections import Counter
  import numpy
  import nltk
label_desc: Use image node_id 4 for calling the SimVectorizer obj's underlying member method code with example usage: obj.vectorization(x) and returns: s, np