class_docstr: Main data class: encapsulate list of utterances, mentions and speakers
  Process utterances to extract mentions and pre-compute mentions features
docstr: Arguments:
      nlp (spaCy Language Class): A spaCy Language Class for processing the text input
      utterances: utterance(s) to load already see self.add_utterances()
      utterances_speaker: speaker(s) of utterance(s) to load already see self.add_utterances()
      speakers_names: speaker(s) of utterance(s) to load already see self.add_utterances()
      blacklist (boolean): use a list of term for which coreference is not preformed
      consider_speakers (boolean): consider speakers informations
      pretrained_model_path (string): Path to a folder with pretrained word embeddings
      embedding_extractor (EmbeddingExtractor): Use a pre-loaded word embeddings extractor
      conll (string): If training on coNLL data: identifier of the document type
      debug (boolean): print debug informations
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code: def __init__(
      self,
      nlp,
      utterances=None,
      utterances_speaker=None,
      speakers_names=None,
      blacklist=False,
      consider_speakers=False,
      model_path=None,
      embedding_extractor=None,
      conll=None,
      debug=False,
  ):
      """
      Arguments:
          nlp (spaCy Language Class): A spaCy Language Class for processing the text input
          utterances: utterance(s) to load already see self.add_utterances()
          utterances_speaker: speaker(s) of utterance(s) to load already see self.add_utterances()
          speakers_names: speaker(s) of utterance(s) to load already see self.add_utterances()
          blacklist (boolean): use a list of term for which coreference is not preformed
          consider_speakers (boolean): consider speakers informations
          pretrained_model_path (string): Path to a folder with pretrained word embeddings
          embedding_extractor (EmbeddingExtractor): Use a pre-loaded word embeddings extractor
          conll (string): If training on coNLL data: identifier of the document type
          debug (boolean): print debug informations
      """
      self.nlp = nlp
      self.blacklist = blacklist
      self.utterances = []
      self.utterances_speaker = []
      self.last_utterances_loaded = []
      self.mentions = []
      self.speakers = {}
      self.n_sents = 0
      self.debug = debug
      self.consider_speakers = consider_speakers or conll is not None
  
      self.genre_, self.genre = self.set_genre(conll)
  
      if model_path is not None and embedding_extractor is None:
          self.embed_extractor = EmbeddingExtractor(model_path)
      elif embedding_extractor is not None:
          self.embed_extractor = embedding_extractor
      else:
          self.embed_extractor = None
  
      if utterances:
          self.add_utterances(
              utterances, utterances_speaker, speakers_names
          )
  
imports: import re
  import io
  from six import string_types, integer_types
  from spacy.tokens import Span, Token
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.utils import encode_distance, parallel_process
  import spacy
  import numpy
label_desc: Use image node_id 1 to create a new Document object from inherited base classes: object with example: obj = Document(nlp, utterances, utterances_speaker, speakers_names, blacklist, consider_speakers, model_path, embedding_extractor, conll, debug)