class_docstr: A specialized corpus view for tagged documents.  It can be
  customized via flags to divide the tagged corpus documents up by
  sentence or paragraph, and to include or omit part of speech tags.
  ``TaggedCorpusView`` objects are typically created by
  ``TaggedCorpusReader`` (not directly by nltk users).
class_docstr_tok: ["A","specialized","corpus","view","for","tagged","documents",".","It","can","be","customized","via","flags","to","divide","the","tagged","corpus","documents","up","by","sentence","or","paragraph",",","and","to","include","or","omit","part","of","speech","tags",".","``","TaggedCorpusView","``","objects","are","typically","created","by","``","TaggedCorpusReader","``","(","not","directly","by","nltk","users",")","."]
code: def __init__(
      self,
      corpus_file,
      encoding,
      tagged,
      group_by_sent,
      group_by_para,
      sep,
      word_tokenizer,
      sent_tokenizer,
      para_block_reader,
      tag_mapping_function=None,
  ):
      self._tagged = tagged
      self._group_by_sent = group_by_sent
      self._group_by_para = group_by_para
      self._sep = sep
      self._word_tokenizer = word_tokenizer
      self._sent_tokenizer = sent_tokenizer
      self._para_block_reader = para_block_reader
      self._tag_mapping_function = tag_mapping_function
      StreamBackedCorpusView.__init__(
          self, corpus_file, encoding=encoding
      )
  
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 TaggedCorpusView object from inherited base classes: StreamBackedCorpusView with example: obj = TaggedCorpusView(corpus_file, encoding, tagged, group_by_sent, group_by_para, sep, word_tokenizer, sent_tokenizer, para_block_reader, tag_mapping_function)