class_docstr: Corpus reader for the York-Toronto-Helsinki Parsed Corpus of Old
  English Prose (YCOE), a 1.5 million word syntactically-annotated
  corpus of Old English prose texts.
class_docstr_tok: ["Corpus","reader","for","the","York-Toronto-Helsinki","Parsed","Corpus","of","Old","English","Prose","(","YCOE",")",",","a","1.5","million","word","syntactically-annotated","corpus","of","Old","English","prose","texts","."]
code: def __init__(self, root, encoding="utf8"):
      CorpusReader.__init__(self, root, [], encoding)
  
      self._psd_reader = YCOEParseCorpusReader(
          self.root.join("psd"), ".*", ".psd", encoding=encoding
      )
      self._pos_reader = YCOETaggedCorpusReader(
          self.root.join("pos"), ".*", ".pos"
      )
  
      # Make sure we have a consistent set of items:
      documents = {f[:-4] for f in self._psd_reader.fileids()}
      if {f[:-4] for f in self._pos_reader.fileids()} != documents:
          raise ValueError(
              'Items in "psd" and "pos" ' "subdirectories do not match."
          )
  
      fileids = sorted(
          ["%s.psd" % doc for doc in documents]
          + ["%s.pos" % doc for doc in documents]
      )
      CorpusReader.__init__(self, root, fileids, encoding)
      self._documents = sorted(documents)
  
imports: import os
  import re
  from nltk.corpus.reader.api import *
  from nltk.corpus.reader.bracket_parse import BracketParseCorpusReader
  from nltk.corpus.reader.tagged import TaggedCorpusReader
  from nltk.corpus.reader.util import *
  from nltk.tokenize import RegexpTokenizer
label_desc: Use image node_id 1 to create a new YCOECorpusReader object from inherited base classes: CorpusReader with example: obj = YCOECorpusReader(root, encoding)