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 _rnn_cell(self, units, **kwargs):
      if self.use_cudnn:
          rnn_cell = tf.keras.layers.CuDNNLSTM(units, **kwargs)
      else:
          rnn_cell = tf.keras.layers.LSTM(units, **kwargs)
      return rnn_cell
  
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 3 for calling the MultiTaskIntentModel obj's underlying member method code with example usage: obj._rnn_cell(units) and returns: rnn_cell