class_docstr: Convert a collection of raw documents to a document id matrix.
  
  Attributes:
      _use_char: boolean. Whether to use char feature.
      _num_norm: boolean. Whether to normalize text.
      _word_vocab: dict. A mapping of words to feature indices.
      _char_vocab: dict. A mapping of chars to feature indices.
      _label_vocab: dict. A mapping of labels to feature indices.
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docstr: Learn vocabulary and return document id matrix.
  
  This is equivalent to fit followed by transform.
  
  Args:
      X : iterable
      an iterable which yields either str, unicode or file objects.
  
  Returns:
      list : document id matrix.
      list: label id matrix.
docstr_tok: ["Learn","vocabulary","and","return","document","id","matrix",".","This","is","equivalent","to","fit","followed","by","transform",".","Args",":","X",":","iterable","an","iterable","which","yields","either","str",",","unicode","or","file","objects",".","Returns",":","list",":","document","id","matrix",".","list",":","label","id","matrix","."]
code: def fit_transform(self, X, y=None, **params):
      """Learn vocabulary and return document id matrix.
  
      This is equivalent to fit followed by transform.
  
      Args:
          X : iterable
          an iterable which yields either str, unicode or file objects.
  
      Returns:
          list : document id matrix.
          list: label id matrix.
      """
      return self.fit(X, y).transform(X, y)
  
imports: from ....None import utils
  from ....imports import *
  from .utils import Vocabulary
label_desc: Use image node_id 12 for calling the IndexTransformer obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self