class_docstr: Doesn't work for multiclass as promised in paper. Needs investigation
  Parameters
  ----------
  norm : 'l1', 'l2', 'max' or None, optional
      Norm used to normalize term vectors. None for no normalization.
  sublinear_tf : boolean, default=False
      Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
  References
  ----------
  .. [0] `https://www.researchgate.net/publication/333231361_    Weighting_Words_Using_Bi-Normal_Separation_for_Text_Classifi     cation_Tasks_with_Multiple_Classes`
class_docstr_tok: ["Does","n't","work","for","multiclass","as","promised","in","paper",".","Needs","investigation","Parameters","--","--","--","--","--","norm",":","'l1","'",",","'l2","'",",","'max","'","or","None",",","optional","Norm","used","to","normalize","term","vectors",".","None","for","no","normalization",".","sublinear_tf",":","boolean",",","default=False","Apply","sublinear","tf","scaling",",","i.e",".","replace","tf","with","1","+","log","(","tf",")",".","References","--","--","--","--","--","..","[","0","]","`","https",":","\/\/www.researchgate.net\/publication\/333231361_","Weighting_Words_Using_Bi-Normal_Separation_for_Text_Classifi","cation_Tasks_with_Multiple_Classes","`"]
code: def transform(self, X):
      if self.sublinear_tf:
          X = ensure_sparse_format(X)
          np.log(X.data, X.data)
          X.data += 1
      f = self._n_features
      X = X * sp.spdiags(self.k, 0, f, f)
      if self.norm:
          X = normalize(X, self.norm, copy=False)
      return X
  
imports: import collections
  import itertools
  import numpy
  import scipy.sparse
  from scipy.stats import norm
  from sklearn.decomposition import TruncatedSVD
  from sklearn.preprocessing import normalize
  from sklearn.base import TransformerMixin, BaseEstimator
  from gensim.models.keyedvectors import BaseKeyedVectors
label_desc: Use image node_id 3 for calling the TfebnsVectorizer obj's underlying member method code with example usage: obj.transform(X) and returns: X