class_docstr: Convert a collection of raw documents to a matrix of TF-IDF features.
  
  Parameters
  ----------
  tokenizer: Tokenizer, optional, default = None
      Tokenizer for text splitting. If None, the BaseTokenizer will be used.
  
  vocab: Vocabulary, optional, default = None
      Vocabulary of tokens. It contains mapping between tokens to their
      integer ids and vice versa.
  
  max_doc_freq: float in range [0.0, 1.0] or int, default=1.0
      When building the vocabulary ignore terms that have a document
      frequency strictly higher than the given threshold (corpus-specific
      stop words).
      If float, the value represents a proportion of documents, int for absolute counts.
      If `vocab` is not None, this will be ignored.
  
  min_doc_freq: float in range [0.0, 1.0] or int, default=1
      When building the vocabulary ignore terms that have a document
      frequency strictly lower than the given threshold. This value is also
      called cut-off in the literature.
      If float, the value represents a proportion of documents, int absolute counts.
      If `vocab` is not None, this will be ignored.
  
  max_features : int or None, optional, default=None
      If not None, build a vocabulary that only consider the top
      `max_features` ordered by term frequency across the corpus.
      If `vocab` is not None, this will be ignored.
  
  binary : boolean, default=False
      If True, all non zero counts are set to 1.
  
  norm : 'l1', 'l2' or None, optional, default='l2'
      Each output row will have unit norm, either:
      * 'l2': Sum of squares of vector elements is 1. The cosine
      similarity between two vectors is their dot product when l2 norm has
      been applied.
      * 'l1': Sum of absolute values of vector elements is 1.
      See :func:`utils.common.normalize`
  
  use_idf : boolean, default=True
      Enable inverse-document-frequency reweighting.
  
  smooth_idf : boolean, default=True
      Smooth idf weights by adding one to document frequencies, as if an
      extra document was seen containing every term in the collection
      exactly once. Prevents zero divisions.
  
  sublinear_tf : boolean (default=False)
      Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
  
  Reference
  ---------
  https://github.com/scikit-learn/scikit-learn/blob/d6d1d63fa6b098c72953a6827aae475f611936ed/sklearn/feature_extraction/text.py#L1451
class_docstr_tok: ["Convert","a","collection","of","raw","documents","to","a","matrix","of","TF-IDF","features",".","Parameters","--","--","--","--","--","tokenizer",":","Tokenizer",",","optional",",","default","=","None","Tokenizer","for","text","splitting",".","If","None",",","the","BaseTokenizer","will","be","used",".","vocab",":","Vocabulary",",","optional",",","default","=","None","Vocabulary","of","tokens",".","It","contains","mapping","between","tokens","to","their","integer","ids","and","vice","versa",".","max_doc_freq",":","float","in","range","[","0.0",",","1.0","]","or","int",",","default=1.0","When","building","the","vocabulary","ignore","terms","that","have","a","document","frequency","strictly","higher","than","the","given","threshold","(","corpus-specific","stop","words",")",".","If","float",",","the","value","represents","a","proportion","of","documents",",","int","for","absolute","counts",".","If","`","vocab","`","is","not","None",",","this","will","be","ignored",".","min_doc_freq",":","float","in","range","[","0.0",",","1.0","]","or","int",",","default=1","When","building","the","vocabulary","ignore","terms","that","have","a","document","frequency","strictly","lower","than","the","given","threshold",".","This","value","is","also","called","cut-off","in","the","literature",".","If","float",",","the","value","represents","a","proportion","of","documents",",","int","absolute","counts",".","If","`","vocab","`","is","not","None",",","this","will","be","ignored",".","max_features",":","int","or","None",",","optional",",","default=None","If","not","None",",","build","a","vocabulary","that","only","consider","the","top","`","max_features","`","ordered","by","term","frequency","across","the","corpus",".","If","`","vocab","`","is","not","None",",","this","will","be","ignored",".","binary",":","boolean",",","default=False","If","True",",","all","non","zero","counts","are","set","to","1.","norm",":","'l1","'",",","'l2","'","or","None",",","optional",",","default='l2'","Each","output","row","will","have","unit","norm",",","either",":","*","'l2","'",":","Sum","of","squares","of","vector","elements","is","1",".","The","cosine","similarity","between","two","vectors","is","their","dot","product","when","l2","norm","has","been","applied",".","*","'l1","'",":","Sum","of","absolute","values","of","vector","elements","is","1",".","See",":","func",":","`","utils.common.normalize","`","use_idf",":","boolean",",","default=True","Enable","inverse-document-frequency","reweighting",".","smooth_idf",":","boolean",",","default=True","Smooth","idf","weights","by","adding","one","to","document","frequencies",",","as","if","an","extra","document","was","seen","containing","every","term","in","the","collection","exactly","once",".","Prevents","zero","divisions",".","sublinear_tf",":","boolean","(","default=False",")","Apply","sublinear","tf","scaling",",","i.e",".","replace","tf","with","1","+","log","(","tf",")",".","Reference","--","--","--","--","-","https",":","\/\/github.com\/scikit-learn\/scikit-learn\/blob\/d6d1d63fa6b098c72953a6827aae475f611936ed\/sklearn\/feature_extraction\/text.py","#","L1451"]
docstr: Transform documents to document-term matrix.
  
  Parameters
  ----------
  raw_documents : List[str]
  
  Returns
  -------
  X : sparse matrix, [n_samples, n_features]
      Tf-idf-weighted document-term matrix.
docstr_tok: ["Transform","documents","to","document-term","matrix",".","Parameters","--","--","--","--","--","raw_documents",":","List","[","str","]","Returns","--","--","--","-","X",":","sparse","matrix",",","[","n_samples",",","n_features","]","Tf-idf-weighted","document-term","matrix","."]
code: def transform(
      self, raw_documents: List[str]
  ) -> (List[List[str]], sp.csr_matrix):
      """Transform documents to document-term matrix.
  
      Parameters
      ----------
      raw_documents : List[str]
  
      Returns
      -------
      X : sparse matrix, [n_samples, n_features]
          Tf-idf-weighted document-term matrix.
  
      """
      _, X = super().transform(raw_documents)
      return self._transform(X)
  
imports: from typing import List, Dict, Callable, Union
  from collections import defaultdict, Counter, OrderedDict
  import string
  import pickle
  import re
  import numpy
  import scipy.sparse
  from .None import FeatureModality
  from .modality import fallback_feature
  from ..utils import normalize
label_desc: Use image node_id 6 for calling the TfidfVectorizer obj's underlying member method code with example usage: obj.transform(raw_documents) and returns: self