class_docstr: Joint Model for Intent classification and slot tagging with inputs of contextual
  information (sequence of utterances) and dictionary feature of the last utterance.
  
  Training data should include:
  doc_label (string): intent classification label of either the sequence of     utterances or just the last sentence
  word_label (string): slot tagging label of the last utterance in the format    of start_idx:end_idx:slot_label, multiple slots are separated by a comma
  text (list of string): sequence of utterances for training
  dict_feat (dict): a dict of features that contains the feature of each word    in the last utterance
  
  Following is an example of raw columns from training data:
  
  ==========  =======================================================================
  doc_label   reply-where
  word_label  10:20:restaurant_name
  text        ["dinner at 6?", "wanna try Tomi Sushi?"]
  dict_feat   {"tokenFeatList": [{"tokenIdx": 2, "features": {"poi:eatery": 0.66}},
                                 {"tokenIdx": 3, "features": {"poi:eatery": 0.66}}]}
  ==========  =======================================================================
code: def get_export_input_names(self, tensorizers):
      return [
          "tokens_vals",
          "seq_tokens_vals",
          "tokens_lens",
          "seq_tokens_lens",
      ]
  
file_path: pytext/pytext/models/seq_models/contextual_intent_slot.py
imports: from typing import Optional
  from pytext.data.tensorizers import SeqTokenTensorizer
  from pytext.models.embeddings import EmbeddingList, WordEmbedding
  from pytext.models.joint_model import IntentSlotModel
  from pytext.models.module import create_module
  from pytext.models.representations.contextual_intent_slot_rep import ContextualIntentSlotRepresentation
label_desc: Use image node_id 3 for calling the ContextualIntentSlotModel obj's underlying member method code with example usage: obj.get_export_input_names(tensorizers) and returns: list