class_docstr: Supervised method (supports multiclass) to transform 
  a count matrix to a normalized Tficf representation
  Tf means term-frequency while ICF means inverse category frequency.
  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://arxiv.org/pdf/1012.2609.pdf`
class_docstr_tok: ["Supervised","method","(","supports","multiclass",")","to","transform","a","count","matrix","to","a","normalized","Tficf","representation","Tf","means","term-frequency","while","ICF","means","inverse","category","frequency",".","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",":","\/\/arxiv.org\/pdf\/1012.2609.pdf","`"]
code: def fit(self, X, y):
      n_samples, n_features = X.shape
      samples = []
      self.number_of_classes = len(np.unique(y))
      for val in range(self.number_of_classes):
          class_mask = sp.spdiags(y == val, 0, n_samples, n_samples)
          samples.append(
              np.bincount(
                  (class_mask * X).indices, minlength=n_features
              )
          )
      samples = np.array(samples)
      self.corpus_occurence = np.sum(samples != 0, axis=0)
      self.k = np.log2(
          1 + (self.number_of_classes / self.corpus_occurence)
      )
      self._n_features = n_features
      return self
  
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 2 for calling the TfIcfVectorizer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self