class_docstr: Reader for corpora that consist of plaintext documents.  Paragraphs
  are assumed to be split using blank lines.  Sentences and words can
  be tokenized using the default tokenizers, or by custom tokenizers
  specified as parameters to the constructor.
  
  This corpus reader can be customized (e.g., to skip preface
  sections of specific document formats) by creating a subclass and
  overriding the ``CorpusView`` class variable.
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docstr: Construct a new plaintext corpus reader for a set of documents
  located at the given root directory.  Example usage:
  
      >>> root = '/usr/local/share/nltk_data/corpora/webtext/'
      >>> reader = PlaintextCorpusReader(root, '.*\.txt') # doctest: +SKIP
  
  :param root: The root directory for this corpus.
  :param fileids: A list or regexp specifying the fileids in this corpus.
  :param word_tokenizer: Tokenizer for breaking sentences or
      paragraphs into words.
  :param sent_tokenizer: Tokenizer for breaking paragraphs
      into words.
  :param para_block_reader: The block reader used to divide the
      corpus into paragraph blocks.
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code: def __init__(
      self,
      root,
      fileids,
      word_tokenizer=WordPunctTokenizer(),
      sent_tokenizer=nltk.data.LazyLoader(
          "tokenizers/punkt/english.pickle"
      ),
      para_block_reader=read_blankline_block,
      encoding="utf8",
  ):
      r"""
      Construct a new plaintext corpus reader for a set of documents
      located at the given root directory.  Example usage:
  
          >>> root = '/usr/local/share/nltk_data/corpora/webtext/'
          >>> reader = PlaintextCorpusReader(root, '.*\.txt') # doctest: +SKIP
  
      :param root: The root directory for this corpus.
      :param fileids: A list or regexp specifying the fileids in this corpus.
      :param word_tokenizer: Tokenizer for breaking sentences or
          paragraphs into words.
      :param sent_tokenizer: Tokenizer for breaking paragraphs
          into words.
      :param para_block_reader: The block reader used to divide the
          corpus into paragraph blocks.
      """
      CorpusReader.__init__(self, root, fileids, encoding)
      self._word_tokenizer = word_tokenizer
      self._sent_tokenizer = sent_tokenizer
      self._para_block_reader = para_block_reader
  
imports: import nltk.data
  from nltk.corpus.reader.api import *
  from nltk.corpus.reader.util import *
  from nltk.tokenize import *
label_desc: Use image node_id 1 to create a new PlaintextCorpusReader object from inherited base classes: CorpusReader with example: obj = PlaintextCorpusReader(root, fileids, word_tokenizer, sent_tokenizer, para_block_reader, encoding)