class_docstr: NER model API
class_docstr_tok: ["NER","model","API"]
code: def vectorize(self, doc, vocab, char_vocab):
      words = np.asarray(
          [vocab[w.lower()] if w.lower() in vocab else 1 for w in doc]
      ).reshape(1, -1)
      sentence_chars = []
      for w in doc:
          word_chars = []
          for c in w:
              if c in char_vocab:
                  _cid = char_vocab[c]
              else:
                  _cid = 1
              word_chars.append(_cid)
          sentence_chars.append(word_chars)
      sentence_chars = np.expand_dims(
          pad_sentences(sentence_chars, self.model.word_length), axis=0
      )
      return words, sentence_chars
  
file_path: nlp-architect/nlp_architect/api/ner_api.py
imports: import pickle
  from os import makedirs, path, sys
  import numpy
  from nlp_architect.api.abstract_api import AbstractApi
  from nlp_architect.models.ner_crf import NERCRF
  from nlp_architect import LIBRARY_OUT
  from nlp_architect.utils.generic import pad_sentences
  from nlp_architect.utils.io import download_unlicensed_file
  from nlp_architect.utils.text import SpacyInstance, bio_to_spans
label_desc: Use image node_id 7 for calling the NerApi obj's underlying member method code with example usage: obj.vectorize(doc, vocab, char_vocab) and returns: words,  sentence_chars