class_docstr: Simple Tensorizer that splits a sentence on spaces and create tensors
  from the vocabulary index built on the training data.
docstr: Look up tokens in vocabulary to get their corresponding index
code: def numberize(self, row):
      """Look up tokens in vocabulary to get their corresponding index"""
      words = self._tokenize(row)
      idx = self.vocab.lookup_all(words)
      # LSTM representations need the length of the sequence
      return idx, len(idx)
  
file_path: pytext/demo/examples/tensorizer.py
imports: from pytext.data.tensorizers import Tensorizer
  from pytext.data.utils import pad_and_tensorize, VocabBuilder
label_desc: Use image node_id 6 for calling the MyWordTensorizer obj's underlying member method code with example usage: obj.numberize(row) and returns: idx,  len