class_docstr: A sequence tagging model for POS and Chunks written in Tensorflow (and Keras) based on the
  paper 'Deep multi-task learning with low level tasks supervised at lower layers'.
  The model has 3 Bi-LSTM layers and outputs POS and Chunk tags.
  
  Args:
      use_cudnn (bool, optional): use GPU based model (CUDNNA cells)
class_docstr_tok: ["A","sequence","tagging","model","for","POS","and","Chunks","written","in","Tensorflow","(","and","Keras",")","based","on","the","paper","'Deep","multi-task","learning","with","low","level","tasks","supervised","at","lower","layers","'",".","The","model","has","3","Bi-LSTM","layers","and","outputs","POS","and","Chunk","tags",".","Args",":","use_cudnn","(","bool",",","optional",")",":","use","GPU","based","model","(","CUDNNA","cells",")"]
docstr: Save the model to disk
  
  Args:
      filepath (str): file name to save model
docstr_tok: ["Save","the","model","to","disk","Args",":","filepath","(","str",")",":","file","name","to","save","model"]
code: def save(self, filepath):
      """
      Save the model to disk
  
      Args:
          filepath (str): file name to save model
      """
      topology = {k: v for k, v in self.__dict__.items()}
      topology.pop("model")
      topology.pop("optimizer")
      topology.pop("use_cudnn")
      save_model(self.model, topology, filepath)
  
file_path: nlp-architect/nlp_architect/models/chunker.py
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 7 for calling the SequenceTagger obj's underlying member method code with example usage: obj.save(filepath) without return types