class_docstr: A reader for part-of-speech tagged corpora whose documents are
  divided into categories based on their file identifiers.
class_docstr_tok: ["A","reader","for","part-of-speech","tagged","corpora","whose","documents","are","divided","into","categories","based","on","their","file","identifiers","."]
docstr: Initialize the corpus reader.  Categorization arguments
  (``cat_pattern``, ``cat_map``, and ``cat_file``) are passed to
  the ``CategorizedCorpusReader`` constructor.  The remaining arguments
  are passed to the ``TaggedCorpusReader``.
docstr_tok: ["Initialize","the","corpus","reader",".","Categorization","arguments","(","``","cat_pattern","``",",","``","cat_map","``",",","and","``","cat_file","``",")","are","passed","to","the","``","CategorizedCorpusReader","``","constructor",".","The","remaining","arguments","are","passed","to","the","``","TaggedCorpusReader","``","."]
code: def __init__(self, *args, **kwargs):
      """
      Initialize the corpus reader.  Categorization arguments
      (``cat_pattern``, ``cat_map``, and ``cat_file``) are passed to
      the ``CategorizedCorpusReader`` constructor.  The remaining arguments
      are passed to the ``TaggedCorpusReader``.
      """
      CategorizedCorpusReader.__init__(self, kwargs)
      TaggedCorpusReader.__init__(self, *args, **kwargs)
  
imports: import os
  from nltk.corpus.reader.api import *
  from nltk.corpus.reader.timit import read_timit_block
  from nltk.corpus.reader.util import *
  from nltk.tag import map_tag, str2tuple
  from nltk.tokenize import *
label_desc: Use image node_id 1 to create a new CategorizedTaggedCorpusReader object from inherited base classes: CategorizedCorpusReader, TaggedCorpusReader with example: obj = CategorizedTaggedCorpusReader()