class_docstr: Given a wordIndex map, transform tokens to corresponding indices.
  Those words not in the map will be aborted.
  Need to tokenize first.
  
  # Arguments
  map: Dict with word (string) as its key and index (int) as its value.
  
  >>> word_indexer = WordIndexer(map={"it": 1, "me": 2})
  creating: createWordIndexer
code: def __init__(self, map, bigdl_type="float"):
      super(WordIndexer, self).__init__(bigdl_type, map)
  
imports: import sys
  import six
  from bigdl.dllib.feature.common import Preprocessing
  from bigdl.dllib.feature.text import TextFeature
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new WordIndexer object from inherited base classes: TextTransformer with example: obj = WordIndexer(map, bigdl_type)