class_docstr: Multi-Task Intent and Slot tagging model (using tf.keras)
  
  Args:
      use_cudnn (bool, optional): use GPU based model (CUDNNA cells)
class_docstr_tok: ["Multi-Task","Intent","and","Slot","tagging","model","(","using","tf.keras",")","Args",":","use_cudnn","(","bool",",","optional",")",":","use","GPU","based","model","(","CUDNNA","cells",")"]
code: def __init__(self, use_cudnn=False):
      super().__init__()
      self.model = None
      self.word_length = None
      self.num_labels = None
      self.num_intent_labels = None
      self.word_vocab_size = None
      self.char_vocab_size = None
      self.word_emb_dims = None
      self.char_emb_dims = None
      self.char_lstm_dims = None
      self.tagger_lstm_dims = None
      self.dropout = None
      self.use_cudnn = use_cudnn
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import tensorflow
  from nlp_architect.nn.tensorflow.python.keras.layers.crf import CRF
  from nlp_architect.nn.tensorflow.python.keras.utils import load_model, save_model
label_desc: Use image node_id 1 to create a new MultiTaskIntentModel object from inherited base classes: IntentExtractionModel with example: obj = MultiTaskIntentModel(use_cudnn)