class_docstr: Object for core-components (POS, Dependency Relations, etc).
  
  Attributes:
      _doc_text: the doc text
      _sentences: list of sentences, each word in a sentence is
          represented by a dictionary, structured as follows: {'start': (int), 'len': (int),
          'pos': (str), 'ner': (str), 'lemma': (str), 'gov': (int), 'rel': (str)}
docstr: Return doc adapted to displacyENT expected input.
docstr_tok: ["Return","doc","adapted","to","displacyENT","expected","input","."]
code: def displacy_doc(self):
      """Return doc adapted to displacyENT expected input."""
      doc = []
      for sentence in self.sentences:
          sentence_doc = {"arcs": [], "words": []}
          # Merge punctuation:
          merged_punct_sentence = merge_punctuation(sentence)
          fix_gov_indexes(merged_punct_sentence, sentence)
          for tok_index, token in enumerate(merged_punct_sentence):
              sentence_doc["words"].append(
                  {"text": token["text"], "tag": token["pos"]}
              )
              dep_tok = tok_index
              gov_tok = token["gov"]
              direction = "left"
              arc_start = dep_tok
              arc_end = gov_tok
              if dep_tok > gov_tok:
                  direction = "right"
                  arc_start = gov_tok
                  arc_end = dep_tok
              if token["gov"] != -1 and token["rel"] != "punct":
                  sentence_doc["arcs"].append(
                      {
                          "dir": direction,
                          "label": token["rel"],
                          "start": arc_start,
                          "end": arc_end,
                      }
                  )
          doc.append(sentence_doc)
      return doc
  
imports: import json
  from nlp_architect.utils.io import validate
label_desc: Use image node_id 16 for calling the CoreNLPDoc obj's underlying member method code with example usage: obj.displacy_doc() and returns: doc