class_docstr: Convert a collection of raw documents to a document id matrix.
  
  Attributes:
      _use_char: boolean. Whether to use char feature.
      _num_norm: boolean. Whether to normalize text.
      _word_vocab: dict. A mapping of words to feature indices.
      _char_vocab: dict. A mapping of chars to feature indices.
      _label_vocab: dict. A mapping of labels to feature indices.
class_docstr_tok: ["Convert","a","collection","of","raw","documents","to","a","document","id","matrix",".","Attributes",":","_use_char",":","boolean",".","Whether","to","use","char","feature",".","_num_norm",":","boolean",".","Whether","to","normalize","text",".","_word_vocab",":","dict",".","A","mapping","of","words","to","feature","indices",".","_char_vocab",":","dict",".","A","mapping","of","chars","to","feature","indices",".","_label_vocab",":","dict",".","A","mapping","of","labels","to","feature","indices","."]
code: def activate_elmo(self):
      if not ALLENNLP_INSTALLED:
          raise Exception(ALLENNLP_ERRMSG)
  
      if not hasattr(self, "elmo"):
          self.elmo = None
      if self.elmo is None:
          self.elmo = Elmo(options_file, weight_file, 2, dropout=0)
      self.use_elmo = True
  
imports: from ....None import utils
  from ....imports import *
  from .utils import Vocabulary
label_desc: Use image node_id 4 for calling the IndexTransformer obj's underlying member method code with example usage: obj.activate_elmo() without return types