class_docstr: A sequence Chunker model written in Tensorflow (and Keras) based SequenceTagger model.
  The model uses only the chunking output of the model.
class_docstr_tok: ["A","sequence","Chunker","model","written","in","Tensorflow","(","and","Keras",")","based","SequenceTagger","model",".","The","model","uses","only","the","chunking","output","of","the","model","."]
docstr: Predict labels given x.
  
  Args:
      x: samples for inference
      batch_size (int, optional): forward pass batch size
  
  Returns:
      tuple of numpy arrays of 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","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 chunk labels
      """
      model = tf.keras.Model(self.model.input, self.model.output[-1])
      return 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 1 for calling the SequenceChunker obj's underlying member method code with example usage: obj.predict(x, batch_size) and returns: model