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: Predict labels given x.
  
  Args:
      x: samples for inference
      batch_size (int, optional): forward pass batch size
  
  Returns:
      tuple of numpy arrays of pos and chunk labels
docstr_tok: ["Predict","labels","given","x.","Args",":","x",":","samples","for","inference","batch_size","(","int",",","optional",")",":","forward","pass","batch","size","Returns",":","tuple","of","numpy","arrays","of","pos","and","chunk","labels"]
code: def predict(self, x, batch_size=1):
      """
      Predict labels given x.
  
      Args:
          x: samples for inference
          batch_size (int, optional): forward pass batch size
  
      Returns:
          tuple of numpy arrays of pos and chunk labels
      """
      return self.model.predict(x=x, batch_size=batch_size)
  
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 6 for calling the SequenceTagger obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: self