class_docstr: Treat a Wikipedia articles dump as a read-only, streamed, memory-efficient corpus.
  
  Supported dump formats:
  
  * <LANG>wiki-<YYYYMMDD>-pages-articles.xml.bz2
  * <LANG>wiki-latest-pages-articles.xml.bz2
  
  The documents are extracted on-the-fly, so that the whole (massive) dump can stay compressed on disk.
  
  Notes
  -----
  Dumps for the English Wikipedia can be founded at https://dumps.wikimedia.org/enwiki/.
  
  Attributes
  ----------
  metadata : bool
      Whether to write articles titles to serialized corpus.
  
  Warnings
  --------
  "Multistream" archives are *not* supported in Python 2 due to `limitations in the core bz2 library
  <https://docs.python.org/2/library/bz2.html#de-compression-of-files>`_.
  
  Examples
  --------
  .. sourcecode:: pycon
  
      >>> from gensim.test.utils import datapath, get_tmpfile
      >>> from gensim.corpora import WikiCorpus, MmCorpus
      >>>
      >>> path_to_wiki_dump = datapath("enwiki-latest-pages-articles1.xml-p000000010p000030302-shortened.bz2")
      >>> corpus_path = get_tmpfile("wiki-corpus.mm")
      >>>
      >>> wiki = WikiCorpus(path_to_wiki_dump)  # create word->word_id mapping, ~8h on full wiki
      >>> MmCorpus.serialize(corpus_path, wiki)  # another 8h, creates a file in MatrixMarket format and mapping
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code: def input(self):
      return self.fname
  
imports: import bz2
  import logging
  import multiprocessing
  import re
  import signal
  from pickle import PicklingError
  from xml.etree.ElementTree import iterparse
  from gensim import utils
  from gensim.corpora.dictionary import Dictionary
  from gensim.corpora.textcorpus import TextCorpus
label_desc: Use image node_id 2 for calling the WikiCorpus obj's underlying member method code with example usage: obj.input() and returns: self