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 transform(self, X, min_freq=1):
      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 TfIcfVectorizer obj's underlying member method code with example usage: obj.transform(X, min_freq) and returns: X